PRELIMINARY DATA PREPARATION

This section includes importing the data, creating of new variables and establishing the dataframes for the initial analysis

Import raw data

dengue_features_test <- read.csv("D:/Google Drive/RYERSON/CKME 136/DengAI/DATASET/dengue_features_test.csv", header = TRUE, stringsAsFactors = FALSE)
dengue_features_train <- read.csv("D:/Google Drive/RYERSON/CKME 136/DengAI/DATASET/dengue_features_train.csv", header = TRUE, stringsAsFactors = FALSE)
dengue_labels_train <- read.csv("D:/Google Drive/RYERSON/CKME 136/DengAI/DATASET/dengue_labels_train.csv", header = TRUE, stringsAsFactors = FALSE)
submission_format <- read.csv("D:/Google Drive/RYERSON/CKME 136/DengAI/DATASET/submission_format.csv", header = TRUE, stringsAsFactors = FALSE)

Convert week_start_date to date format

dengue_features_test$week_start_date <- as.Date(dengue_features_test$week_start_date, "%Y-%m-%d")
dengue_features_train$week_start_date <- as.Date(dengue_features_train$week_start_date, "%Y-%m-%d")

Convert city to factor

dengue_features_test$city <- as.factor(dengue_features_test$city)
dengue_features_train$city <- as.factor(dengue_features_train$city)

Rescale the variables so that it is all in Celcius and mm

dengue_features_train$reanalysis_dew_point_temp_k <- dengue_features_train$reanalysis_dew_point_temp_k - 273.15
dengue_features_test$reanalysis_dew_point_temp_k <- dengue_features_test$reanalysis_dew_point_temp_k - 273.15

dengue_features_train$reanalysis_air_temp_k <- dengue_features_train$reanalysis_air_temp_k - 273.15
dengue_features_test$reanalysis_air_temp_k <- dengue_features_test$reanalysis_air_temp_k - 273.15

dengue_features_train$reanalysis_max_air_temp_k <- dengue_features_train$reanalysis_max_air_temp_k - 273.15
dengue_features_test$reanalysis_max_air_temp_k <- dengue_features_test$reanalysis_max_air_temp_k - 273.15

dengue_features_train$reanalysis_min_air_temp_k <- dengue_features_train$reanalysis_min_air_temp_k - 273.15
dengue_features_test$reanalysis_min_air_temp_k <- dengue_features_test$reanalysis_min_air_temp_k - 273.15

dengue_features_train$reanalysis_avg_temp_k <- dengue_features_train$reanalysis_avg_temp_k - 273.15
dengue_features_test$reanalysis_avg_temp_k <- dengue_features_test$reanalysis_avg_temp_k - 273.15

#!!!tdtr does not appear to be in Kelvin
# dengue_features_train$reanalysis_tdtr_k <- dengue_features_train$reanalysis_tdtr_k - 273.15
# dengue_features_test$reanalysis_tdtr_k <- dengue_features_test$reanalysis_tdtr_k - 273.15

summary(dengue_features_train$reanalysis_dew_point_temp_k)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.49   20.97   22.49   22.10   23.31   25.30      10
summary(dengue_features_train$reanalysis_air_temp_k)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   21.49   24.51   25.50   25.55   26.68   29.05      10
summary(dengue_features_train$reanalysis_max_air_temp_k)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   24.65   27.85   29.25   30.28   32.35   40.85      10
summary(dengue_features_train$reanalysis_min_air_temp_k)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   13.75   20.75   23.05   22.57   24.75   26.75      10
summary(dengue_features_train$reanalysis_avg_temp_k)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   21.74   25.11   26.14   26.08   27.06   29.78      10
summary(dengue_features_train$reanalysis_tdtr_k)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.357   2.329   2.857   4.904   7.625  16.029      10

Merge test and train set without the total_cases

df <- rbind(dengue_features_train,dengue_features_test)

Divide the test into sj and iq separately

iq_features_test <- dengue_features_test[dengue_features_test$city == 'iq', ]
sj_features_test <- dengue_features_test[dengue_features_test$city == 'sj', ]

Divide training sets into sj and iq separately

iq_features_train <- dengue_features_train[dengue_features_train$city == 'iq', ]
sj_features_train <- dengue_features_train[dengue_features_train$city == 'sj', ]

Divide the labels sets into sj and iq separately

iq_labels_train <- dengue_labels_train[dengue_labels_train$city == 'iq', ]
sj_labels_train <- dengue_labels_train[dengue_labels_train$city == 'sj', ]

Merge test and training sets separately for each city without total_cases

sj <- rbind(sj_features_train,sj_features_test)
iq <- rbind(iq_features_train,iq_features_test)

Merge train and label sets to include total_cases

df_train_labels <- merge(dengue_features_train, dengue_labels_train, by=c("city","year","weekofyear"))

Merge test and training sets separately for each city including total_cases

sj_train_labels <- merge(sj_features_train, sj_labels_train, by=c("city","year","weekofyear"))
iq_train_labels <- merge(iq_features_train, iq_labels_train, by=c("city","year","weekofyear"))

INITIAL & EXPLORATORY ANALYSIS

In this section, we summary the value of the data frames (together and by city). We also create the following graphs

  1. Frequency histograms
  2. Bivariate analysis - line graphs for time analysis
  3. Bivariate analysis - scatterplot for total_cases by other variables
  4. Wilcoxon test for test of means between cities

Review summary stats for each city

library(psych)

df_test.summary <- psych::describe(dengue_features_test, IQR=TRUE, quant=c(.25,.75) )
#View(df_test.summary)

df_train.summary <- psych::describe(dengue_features_train, IQR=TRUE, quant=c(.25,.75) )
#View(df_train.summary)

sj_train.summary <- psych::describe(sj_train_labels, IQR=TRUE, quant=c(.25,.75) )
#View(sj_train.summary)

iq_train.summary <- psych::describe(iq_train_labels, IQR=TRUE, quant=c(.25,.75) )
#View(iq_train.summary)


df.summary <- psych::describe(df, IQR=TRUE, quant=c(.25,.75))
#View(df.summary)

sj.summary <- psych::describe(sj, IQR=TRUE, quant=c(.25,.75) )
#View(sj.summary)

iq.summary <- psych::describe(iq, IQR=TRUE, quant=c(.25,.75) )
#View(iq.summary)

# summary(dengue_features_test$week_start_date)
# summary(dengue_features_train$week_start_date)
# summary(iq_features_train$week_start_date)
# summary(iq_features_test$week_start_date)
# summary(sj_features_train$week_start_date)
# summary(sj_features_test$week_start_date)
 
rm(df_test.summary, df_train.summary, sj_train.summary, iq_train.summary, df.summary, sj.summary, iq.summary)

GRAPH: Frequency histogram of all variables in training set (both cities together)

These graphs only include data from the training set as it includes total cases. Climate data across both training and test sets are below.

#remove week_start_date for histogram

df_train_labels$week_start_date <- NULL

cnames <- colnames(df_train_labels) 
par(mfrow=c(2,2))
for (i in 4:ncol(df_train_labels)) {
 hist(df_train_labels[,i],
      breaks = 20,
      main = paste("Freq Histogram", cnames[i], sep = ": "),
      xlab = cnames[i])
}

rm(cnames, i)

GRAPH: Frequency histogram of all variables in training set for SJ

Same as above but only for SJ.

cnames <- colnames(df_train_labels) 
par(mfrow=c(2,2))
for (i in 4:ncol(df_train_labels)) {
 hist(df_train_labels[df_train_labels$city == "sj",i], 
      breaks = 20,
      xlab = cnames[i], 
      main = paste("Freq Histogram for SJ", cnames[i], sep = ": "))
}

rm(cnames, i)

GRAPH: Frequency histogram of all variables in training set for IQ

Same as above but only for IQ.

cnames <- colnames(df_train_labels) 
par(mfrow=c(2,2))
for (i in 4:(ncol(df_train_labels))) {
 hist(df_train_labels[df_train_labels$city == "iq",i],
      breaks = 20,
      xlab = cnames[i],
      main = paste("Freq Histogram for IQ", cnames[i], sep = ": "))
}

rm(cnames, i)

GRAPH: Climate variables by time for SJ

Includes all the data from test and training set by time for SJ therefore the total_cases in not included. Total_cases by time is done separately.

cnames <- colnames(sj) 
par(mfrow=c(2,2))
for (i in 5:(ncol(sj))) {
 plot(sj$week_start_date,sj[,i],
      type = "n",
      ylim = c(min(sj[,i],na.rm=TRUE), max(sj[,i],na.rm=TRUE)),
      ylab = cnames[i],
      main = paste("Time Analysis for SJ", cnames[i], sep = ": "))
 lines(sj$week_start_date,sj[,i])
}

rm(cnames, i)

GRAPH: Climate variables by time for IQ

Includes all the data from test and training set by time for I therefore the total_cases in not included. Total_cases by time is done separately.

cnames <- colnames(iq) 
par(mfrow=c(2,2))
for (i in 5:(ncol(iq))) {
 plot(iq$week_start_date,iq[,i],
      type = "n",
      ylim = c(min(iq[,i],na.rm=TRUE), max(iq[,i],na.rm=TRUE)),
      ylab = cnames[i],
      main = paste("Time Analysis for IQ", cnames[i], sep = ": "))
 lines(iq$week_start_date,iq[,i])
}

rm(cnames, i)

GRAPH: Climate variables by week for SJ

Includes all the data from test and training set by time for SJ therefore the total_cases in not included. Total_cases by time is done separately.

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.4.4
## 
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
## 
##     %+%, alpha
cnames <- colnames(sj) 
par(mfrow=c(2,2))
for (i in 5:(ncol(sj))) {
  gg1 <- ggplot(sj,
                aes(x=weekofyear, 
                    y = sj[,i], 
                    group = weekofyear)) +
    geom_boxplot() +
    scale_x_continuous(breaks=seq(1,52,1)) +
    ylab(cnames[i]) +
    ggtitle(paste(cnames[i])) 

    print(gg1)
  }
## Warning: Removed 234 rows containing non-finite values (stat_boxplot).

## Warning: Removed 60 rows containing non-finite values (stat_boxplot).

## Warning: Removed 20 rows containing non-finite values (stat_boxplot).

## Warning: Removed 20 rows containing non-finite values (stat_boxplot).

## Warning: Removed 11 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 11 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

rm(cnames, i, gg1)

GRAPH: Climate variables by week for IQ

Includes all the data from test and training set by time for I therefore the total_cases in not included. Total_cases by time is done separately.

library(ggplot2)

cnames <- colnames(iq) 
par(mfrow=c(2,2))
for (i in 5:(ncol(iq))) {
  gg1 <- ggplot(sj,
                aes(x=weekofyear, 
                    y = sj[,i], 
                    group = weekofyear)) +
    geom_boxplot() +
    scale_x_continuous(breaks=seq(1,52,1)) +
    ylab(cnames[i]) +
    ggtitle(paste(cnames[i])) 

    print(gg1)
  }
## Warning: Removed 234 rows containing non-finite values (stat_boxplot).

## Warning: Removed 60 rows containing non-finite values (stat_boxplot).

## Warning: Removed 20 rows containing non-finite values (stat_boxplot).

## Warning: Removed 20 rows containing non-finite values (stat_boxplot).

## Warning: Removed 11 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 11 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing non-finite values (stat_boxplot).

rm(cnames, i, gg1)

GRAPH: Total_cases by time for SJ, IQ and together

Line graph of all data by total cases. This uses only the training set.

library(ggplot2)

df_train_labels <- merge(dengue_features_train, dengue_labels_train, by=c("city","year","weekofyear"))

par(mfcol=c(1,3))
# Dengue Cases both cities together
ggplot(data = df_train_labels, aes(x=week_start_date, y=total_cases)) +
       geom_bar(stat = "identity", fill = "purple") +
  labs(title = "Total Dengue Cases - both cities combined",
       subtitle = paste(min(df_train_labels$week_start_date),max(df_train_labels$week_start_date), sep = " to "),
       x = "Date", y = "Total dengue cases")

#Dengue Cases for San Jose
ggplot(data = df_train_labels[df_train_labels$city == "sj",], aes(x=week_start_date, y=total_cases)) +
       geom_bar(stat = "identity", fill = "blue") +
  labs(title = "Total Dengue Cases in San Jose",
       subtitle = paste(min(df_train_labels$week_start_date[df_train_labels$city == "sj"]),max(df_train_labels$week_start_date[df_train_labels$city == "sj"]), sep = " to "),
       x = "Date", y = "Total dengue cases")

# Dengue Cases for Iquitos
ggplot(data = df_train_labels[df_train_labels$city == "iq",], aes(x=week_start_date, y=total_cases)) +
       geom_bar(stat = "identity", fill = "green") +
  labs(title = "Total Dengue Cases in Iquitos",
       subtitle = paste(min(df_train_labels$week_start_date[df_train_labels$city == "iq"]),max(df_train_labels$week_start_date[df_train_labels$city == "iq"]), sep = " to "),
       x = "Date", y = "Total dengue cases")

GRAPH: Average Total_cases by week for SJ, IQ

Line graph of all data by total cases. This uses only the training set.

library(ggplot2)

gg1 <- ggplot(sj_train_labels,
                aes(x=weekofyear, 
                    y = total_cases, 
                    group = weekofyear)) +
    geom_boxplot() +
    scale_x_continuous(breaks=seq(1,52,1)) +
  stat_summary(fun.y=mean, geom="point", shape=20, size=3, color="red", fill="red") +
    ylab("Total cases") +
    ggtitle(paste("Boxplot: Total cases by Week for SJ")) 

    print(gg1)

gg3 <- ggplot(data=sj_labels_train, aes(x=weekofyear, y=total_cases)) +
  geom_bar(stat="summary", fun.y = "mean") +
  ggtitle(paste("Bar graph: Average total cases by Week for SJ")) +
  scale_x_continuous(breaks = seq(1,52, 2))

print(gg3)

gg2 <- ggplot(iq_train_labels,
                aes(x=weekofyear, 
                    y = total_cases, 
                    group = weekofyear)) +
    geom_boxplot() +
    scale_x_continuous(breaks=seq(1,52,1)) +
  stat_summary(fun.y=mean, geom="point", shape=20, size=3, color="red", fill="red") +
    ylab("Total cases") +
    ggtitle(paste("Boxplot: Total cases by Week for IQ")) 

    print(gg2)

gg4 <- ggplot(data=iq_labels_train, aes(x=weekofyear, y=total_cases)) +
  geom_bar(stat="summary", fun.y = "mean") +
  ggtitle(paste("Bar graph: Average total cases by Week for IQ")) +
  scale_x_continuous(breaks = seq(1,52, 2))

print(gg4)

    rm(gg1, gg2, gg3, gg4)

GRAPH: Total_cases by climate variables (both cities together)

Scatterplot using training set only.

cnames <- colnames(df_train_labels) 
par(mfrow=c(2,2))
for (i in 5:(ncol(df_train_labels)-1)) {
 plot(df_train_labels$total_cases,
      df_train_labels[,i], 
      cex = 0.5, 
      pch = 19,
      ylim = c(min(df_train_labels[,i],na.rm=TRUE), max(df_train_labels[,i],na.rm=TRUE)),
      main = paste("Total_cases by climate variables", cnames[i], sep = ": "),
      ylab = cnames[i])
 
}

rm(cnames, i)

GRAPH: Total_cases by climate variables for SJ

Same as above but for SJ

cnames <- colnames(df_train_labels) 
par(mfrow=c(2,2))
for (i in 5:(ncol(df_train_labels)-1)) {
 plot(df_train_labels$total_cases[df_train_labels$city == "sj"],
      df_train_labels[df_train_labels$city == "sj",i], 
      cex = 0.5, 
      pch = 19,
      ylim = c(min(df_train_labels[,i],na.rm=TRUE), max(df_train_labels[,i],na.rm=TRUE)),
      main = paste("Total_cases for SJ by climate variables", cnames[i], sep = ": "),
      ylab = cnames[i])
 
}

rm(cnames, i)

GRAPH: Total_cases by climate variables for IQ

Same as above but for IQ.

cnames <- colnames(df_train_labels) 
par(mfrow=c(2,2))
for (i in 5:(ncol(df_train_labels)-1)) {
 plot(df_train_labels$total_cases[df_train_labels$city == "iq"],
      df_train_labels[df_train_labels$city == "iq",i], 
      cex = 0.5, 
      pch = 19,
      ylim = c(min(df_train_labels[,i],na.rm=TRUE), max(df_train_labels[,i],na.rm=TRUE)),
      main = paste("Total_cases for IQ by climate variables", cnames[i], sep = ": "),
      ylab = cnames[i])
 
}

rm(cnames, i)

Compare the means between same variables in different cities

We can see that the same feature is significantly different in each city

cnames <- colnames(sj_train_labels)
for (i in 5:(ncol(sj_train_labels))){
  wilt <- wilcox.test(sj_train_labels[,i],iq_train_labels[,i])  
  print(cnames[i])
  print(wilt)
}
## [1] "ndvi_ne"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 21691, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "ndvi_nw"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 32596, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "ndvi_se"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 107990, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "ndvi_sw"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 78560, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "precipitation_amt_mm"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 118470, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "reanalysis_air_temp_k"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 369950, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "reanalysis_avg_temp_k"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 255790, p-value = 0.03716
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "reanalysis_dew_point_temp_k"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 208230, p-value = 3.071e-05
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "reanalysis_max_air_temp_k"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 4645.5, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "reanalysis_min_air_temp_k"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 474700, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "reanalysis_precip_amt_kg_per_m2"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 139740, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "reanalysis_relative_humidity_percent"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 62770, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "reanalysis_sat_precip_amt_mm"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 118470, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "reanalysis_specific_humidity_g_per_kg"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 192510, p-value = 4.502e-10
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "reanalysis_tdtr_k"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 22, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "station_avg_temp_c"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 183690, p-value = 1.887e-08
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "station_diur_temp_rng_c"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 6834, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "station_max_temp_c"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 59998, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "station_min_temp_c"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 361870, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "station_precip_mm"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 142000, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
## 
## [1] "total_cases"
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  sj_train_labels[, i] and iq_train_labels[, i]
## W = 401310, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
rm(cnames, i, wilt)

Compare similar variable values within the dataset

There are several variables which appear to be the same feature but taken from a different source. For example, station_precip_mm and precipitation_amt_mm and reanalysis_sat_precip_amt_mm all appear to be the same “Total Precipitation value” Only one should be kept if they are the same.

Difference in max air temp

“station_max_temp_c”" and “reanalysis_max_air_temp_k” (scaled to Celcius)

library(ggplot2)

#generate a difference in max temp variable
sj_train_labels$max_air_diff <- sj_train_labels$station_max_temp_c - sj_train_labels$reanalysis_max_air_temp_k

#barplot the difference by year
ggplot(sj_train_labels,aes(x=year, y=max_air_diff))+
  geom_bar(stat='identity')
## Warning: Removed 6 rows containing missing values (position_stack).

#box plot difference by year
ggplot(sj_train_labels, aes(x=year, y = max_air_diff, group = year)) +   geom_boxplot() 
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

#Add month to the dataframe
sj_train_labels$month <- as.POSIXlt(sj_train_labels$week_start_date)$mon +1

#box plot difference by month
ggplot(sj_train_labels, aes(x=month, y = max_air_diff, group = month)) +   geom_boxplot() + scale_x_continuous(breaks=seq(1,12,1))
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

sj_train_labels$max_air_diff <- NULL
sj_train_labels$month <- NULL

Difference in min air temp

“station_min_temp_c”" and “reanalysis_min_air_temp_k” (scaled to Celcius)

library(ggplot2)

#generate a difference in max temp variable
sj_train_labels$min_air_diff <- sj_train_labels$station_min_temp_c - sj_train_labels$reanalysis_min_air_temp_k

#barplot the difference by year
ggplot(sj_train_labels,aes(x=year, y=min_air_diff))+
  geom_bar(stat='identity')
## Warning: Removed 6 rows containing missing values (position_stack).

#box plot difference by year
ggplot(sj_train_labels, aes(x=year, y = min_air_diff, group = year)) +   geom_boxplot() 
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

#Add month to the dataframe
sj_train_labels$month <- as.POSIXlt(sj_train_labels$week_start_date)$mon +1

#box plot difference by month
ggplot(sj_train_labels, aes(x=month, y = min_air_diff, group = month)) +   geom_boxplot() + scale_x_continuous(breaks=seq(1,12,1))
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

sj_train_labels$min_air_diff <- NULL
sj_train_labels$month <- NULL

Difference in average air temp

“station_avg_temp_c”" and “reanalysis_avg_temp_k” (scaled to Celcius)

library(ggplot2)

#generate a difference in max temp variable
sj_train_labels$avg_air_diff <- sj_train_labels$station_avg_temp_c - sj_train_labels$reanalysis_avg_temp_k

#barplot the difference by year
ggplot(sj_train_labels,aes(x=year, y=avg_air_diff))+
  geom_bar(stat='identity')
## Warning: Removed 6 rows containing missing values (position_stack).

#box plot difference by year
ggplot(sj_train_labels, aes(x=year, y = avg_air_diff, group = year)) +   geom_boxplot() 
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

#Add month to the dataframe
sj_train_labels$month <- as.POSIXlt(sj_train_labels$week_start_date)$mon +1

#box plot difference by month
ggplot(sj_train_labels, aes(x=month, y = avg_air_diff, group = month)) +   geom_boxplot() + scale_x_continuous(breaks=seq(1,12,1))
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

sj_train_labels$avg_air_diff <- NULL
sj_train_labels$month <- NULL

Difference in total precipitation

“station_precip_mm”, “precipitation_amt_mm”, “reanalysis_sat_precip_amt_mm”, “reanalysis_precip_amt_kg_per_m2”

library(ggplot2)

precip <- c("station_precip_mm", "precipitation_amt_mm", "reanalysis_sat_precip_amt_mm", "reanalysis_precip_amt_kg_per_m2")

#Add month to the dataframe
sj_train_labels$month <- as.POSIXlt(sj_train_labels$week_start_date)$mon +1



for (i in 1:3){
  par(mfrow=c(1,3))
  #generate the first variable in the list
  p1 <- precip[i]
  ind1 <- which(colnames(sj_train_labels)==p1)
  for (j in ((i+1):4)){
    #generate the next variable in the list
    p2 <- precip[j]
  ind2 <- which(colnames(sj_train_labels)==p2)
  #generate a difference variable 
   sj_train_labels$diff <- sj_train_labels[,ind1] - sj_train_labels[,ind2]
   
   #barplot the difference by year
   gg1 <-ggplot(sj_train_labels,
                 aes(x=year, y=diff))+
      geom_bar(stat = "identity", fill="steelblue") + 
      ggtitle(paste(p1, "&", p2))
    print(gg1)
    
    #box plot the difference by year
   gg2 <-ggplot(sj_train_labels,
                 aes(x=year, y=diff, group = year)) +
      geom_boxplot() + 
      ggtitle(paste(p1, "&", p2))
    print(gg2)
    
    #box plot difference by month
    gg3 <- ggplot(sj_train_labels, 
                  aes(x=month, y = diff, group = month)) +
      geom_boxplot() +
      scale_x_continuous(breaks=seq(1,12,1)) +
      ggtitle(paste(p1, "&", p2))
    print(gg3)
  }
}
## Warning: Removed 9 rows containing missing values (position_stack).

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing missing values (position_stack).

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning: Removed 6 rows containing missing values (position_stack).

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning: Removed 6 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing missing values (position_stack).

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing missing values (position_stack).

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing missing values (position_stack).

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing non-finite values (stat_boxplot).

sj_train_labels$diff <- NULL
sj_train_labels$month <- NULL 

rm(gg1, gg2, gg3, i, ind1, ind2, j, p1, p2, precip)

Review of climate factors independently (SJ ONLY)

This section of the exploratory analysis will review the effects of the major components of climate affect dengue cases. A 5x cross validation decision tree algorithm will be used to review the MAE error by year.

Decision Tree with vegetation data

library(caret)
## Warning: package 'caret' was built under R version 3.4.4
## Loading required package: lattice
library(rpart)

set.seed(136)

performetrics <- data.frame()

#trainControl
control <- trainControl(method="repeatedcv", number=5, repeats=3)

model_sj.veg <- train(total_cases ~ ndvi_se + ndvi_sw + ndvi_ne + ndvi_nw, 
                       data=sj_train_labels,
                       method="rpart",
                       trControl=control,
                      na.action = na.pass)
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
  # summarize results
  performetrics[1,1] <- "Veg"
  performetrics[1,2] <- min(model_sj.veg$results["MAE"])
  performetrics[1,3] <- min(model_sj.veg$results["RMSE"])  


colnames(performetrics)[1]<- "Climate"
colnames(performetrics)[2]<- "MAE"
colnames(performetrics)[3]<- "RMSE"

performetrics
##   Climate      MAE     RMSE
## 1     Veg 26.96734 51.45264
rm(control, model_sj.veg, performetrics)

Decision Tree with temperature data

library(caret)
library(rpart)

set.seed(136)

performetrics <- data.frame()

#trainControl
control <- trainControl(method="repeatedcv", number=5, repeats=3)

model_sj.temp <- train(total_cases ~ station_max_temp_c + station_min_temp_c  + station_avg_temp_c  + station_diur_temp_rng_c + reanalysis_dew_point_temp_k  + reanalysis_air_temp_k + reanalysis_max_air_temp_k + reanalysis_min_air_temp_k + reanalysis_avg_temp_k + reanalysis_tdtr_k + ndvi_nw, 
                       data=sj_train_labels,
                       method="rpart",
                       trControl=control,
                      na.action = na.pass)
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
  # summarize results
  performetrics[1,1] <- "Temperature"
  performetrics[1,2] <- min(model_sj.temp$results["MAE"])
  performetrics[1,3] <- min(model_sj.temp$results["RMSE"])  


colnames(performetrics)[1]<- "Climate"
colnames(performetrics)[2]<- "MAE"
colnames(performetrics)[3]<- "RMSE"

performetrics
##       Climate      MAE     RMSE
## 1 Temperature 28.23282 50.77001
rm(control, model_sj.temp, performetrics)

Decision Tree with precipitation data

library(caret)
library(rpart)

set.seed(136)

performetrics <- data.frame()

#trainControl
control <- trainControl(method="repeatedcv", number=5, repeats=3)

model_sj.precip <- train(total_cases ~ station_precip_mm  + precipitation_amt_mm + reanalysis_sat_precip_amt_mm + reanalysis_precip_amt_kg_per_m2, 
                       data=sj_train_labels,
                       method="rpart",
                       trControl=control,
                      na.action = na.pass)
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
  # summarize results
  performetrics[1,1] <- "Precipitation"
  performetrics[1,2] <- min(model_sj.precip$results["MAE"])
  performetrics[1,3] <- min(model_sj.precip$results["RMSE"])  


colnames(performetrics)[1]<- "Climate"
colnames(performetrics)[2]<- "MAE"
colnames(performetrics)[3]<- "RMSE"

performetrics
##         Climate    MAE    RMSE
## 1 Precipitation 28.489 50.7481
rm(control, model_sj.precip, performetrics)

Decision Tree with humidity data

library(caret)
library(rpart)

set.seed(136)

performetrics <- data.frame()

#trainControl
control <- trainControl(method="repeatedcv", number=5, repeats=3)

model_sj.humid <- train(total_cases ~ reanalysis_relative_humidity_percent + reanalysis_specific_humidity_g_per_kg , 
                       data=sj_train_labels,
                       method="rpart",
                       trControl=control,
                      na.action = na.pass)
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
  # summarize results
  performetrics[1,1] <- "Humidity"
  performetrics[1,2] <- min(model_sj.humid$results["MAE"])
  performetrics[1,3] <- min(model_sj.humid$results["RMSE"])  


colnames(performetrics)[1]<- "Climate"
colnames(performetrics)[2]<- "MAE"
colnames(performetrics)[3]<- "RMSE"

performetrics
##    Climate      MAE    RMSE
## 1 Humidity 27.87611 50.2604
rm(control, model_sj.humid, performetrics)

ANALYSIS OF OUTLIERS

GRAPH: Boxplot of climate variables (test and train)

Boxplot includes test and training set - NA still included

library(ggplot2)
cnames <- colnames(df) 
for (i in 5:(ncol(df))) {
 p <- ggplot(df, aes(x=city, y = df[,i], fill = city)) + 
  geom_boxplot() +
   labs(title = "Boxplot of climate variables",
       subtitle = cnames[i],
       x = "City", y = cnames[i])
 print(p)
}
## Warning: Removed 237 rows containing non-finite values (stat_boxplot).

## Warning: Removed 63 rows containing non-finite values (stat_boxplot).

## Warning: Removed 23 rows containing non-finite values (stat_boxplot).

## Warning: Removed 23 rows containing non-finite values (stat_boxplot).

## Warning: Removed 15 rows containing non-finite values (stat_boxplot).

## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

## Warning: Removed 15 rows containing non-finite values (stat_boxplot).

## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

## Warning: Removed 55 rows containing non-finite values (stat_boxplot).

## Warning: Removed 55 rows containing non-finite values (stat_boxplot).

## Warning: Removed 23 rows containing non-finite values (stat_boxplot).

## Warning: Removed 23 rows containing non-finite values (stat_boxplot).

## Warning: Removed 27 rows containing non-finite values (stat_boxplot).

rm(cnames, i, p)

GRAPH: Boxplot of total cases

library(ggplot2)
ggplot(df_train_labels, aes(x=city, y = total_cases, fill = city)) + 
  geom_boxplot() +
   labs(title = "Boxplot of Total_cases",
       x = "City", y = "Total_cases")

DATAFRAME CLEANUP 1

Clean up all the extra dataframes produced during the exploratory analysis

rm(dengue_features_test,
   dengue_features_train,
   dengue_labels_train,
   sj_features_test,
   sj_features_train,
   sj_labels_train,
   iq_features_test,
   iq_features_train,
   iq_labels_train,
   df,
   iq,
   sj,
   df_train_labels,
   submission_format
    )

MISSING VALUES

Check for missing values (is.na)

In this section, we look at the number of missing values. Later we will do something about these missing values.

sj_train.na <- sapply(sj_train_labels, function(x) sum(is.na (x)))

iq_train.na <- sapply(iq_train_labels, function(x) sum(is.na (x)))

#df_train_labels.na <- sum(is.na(df_train_labels$total_cases))
# View(sj_train.na)
# View(iq_train.na)


#df_train_labels.na
rm(sj_train.na)
rm(iq_train.na)
#rm(df_train_labels.na)

Missing values: Remove all rows with an NA in it

sj_train_labels.naomit <- na.omit(sj_train_labels)
iq_train_labels.naomit <- na.omit(iq_train_labels)

Missing values: Using last non-NA value

library(zoo)
#library(tidyverse)
library(plyr)
sj_train_labels <- sj_train_labels[order(sj_train_labels$year, sj_train_labels$weekofyear),]
iq_train_labels <- iq_train_labels[order(iq_train_labels$year, iq_train_labels$weekofyear),]

sj_train_labels.lastna <- sj_train_labels 
iq_train_labels.lastna <-iq_train_labels 

sj_train_labels.lastna <- colwise(na.locf)(sj_train_labels.lastna)
iq_train_labels.lastna <- colwise(na.locf)(iq_train_labels.lastna)

#Issues using tidyverse as the locf function converts all values to character
# sj_train_labels.lastna <- sj_train_labels.lastna %>% do(na.locf(.))
# iq_train_labels.lastna <-iq_train_labels.lastna %>% do(na.locf(.))

sum(is.na(sj_train_labels.lastna))
sum(is.na(iq_train_labels.lastna))

REMOVE week_start_date AND city FROM DATASET

Removing the city and the week_start_date from the dataset wil allow for easier analysis

#keep a version with the start week included
sj_train_labels.startweek <- sj_train_labels.lastna
iq_train_labels.startweek <- iq_train_labels.lastna

#remove city
sj_train_labels.naomit$city <- NULL
sj_train_labels.lastna$city <- NULL
sj_train_labels.startweek$city <- NULL

iq_train_labels.naomit$city <- NULL
iq_train_labels.lastna$city <- NULL
iq_train_labels.startweek$city <- NULL

#remove week_start_date
sj_train_labels.naomit$week_start_date <- NULL
sj_train_labels.lastna$week_start_date <- NULL

iq_train_labels.naomit$week_start_date <- NULL
iq_train_labels.lastna$week_start_date <- NULL

CORRELATION ANALYSIS

In this section, we look at the correlation between the total_cases and the climate variables.

First we need to remove any of the non-numeric variables. The missing values are still in this first correlation analysis but this will be repeated with the missing values included.

Correlation analysis before missing values are addressed

library(tidyverse)
## Warning: package 'tidyverse' was built under R version 3.4.4
## -- Attaching packages ---------------------------------------------------------------- tidyverse 1.2.1 --
## v tibble  1.4.2     v purrr   0.2.4
## v tidyr   0.8.1     v dplyr   0.7.4
## v readr   1.1.1     v stringr 1.2.0
## v tibble  1.4.2     v forcats 0.3.0
## Warning: package 'tibble' was built under R version 3.4.4
## Warning: package 'tidyr' was built under R version 3.4.4
## Warning: package 'forcats' was built under R version 3.4.4
## -- Conflicts ------------------------------------------------------------------- tidyverse_conflicts() --
## x ggplot2::%+%()     masks psych::%+%()
## x ggplot2::alpha()   masks psych::alpha()
## x dplyr::arrange()   masks plyr::arrange()
## x purrr::compact()   masks plyr::compact()
## x dplyr::count()     masks plyr::count()
## x dplyr::failwith()  masks plyr::failwith()
## x dplyr::filter()    masks stats::filter()
## x dplyr::id()        masks plyr::id()
## x dplyr::lag()       masks stats::lag()
## x purrr::lift()      masks caret::lift()
## x dplyr::mutate()    masks plyr::mutate()
## x dplyr::rename()    masks plyr::rename()
## x dplyr::summarise() masks plyr::summarise()
## x dplyr::summarize() masks plyr::summarize()
library(corrplot)
## Warning: package 'corrplot' was built under R version 3.4.4
## corrplot 0.84 loaded
library(RColorBrewer)
require(gridExtra)
## Loading required package: gridExtra
## Warning: package 'gridExtra' was built under R version 3.4.4
## 
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
## 
##     combine
sj_train_labels %>% 
  dplyr::select(-city, -year, -weekofyear, -week_start_date) %>%
  cor(use = 'pairwise.complete.obs') -> M1

corrplot(M1, type="lower", method="color",
           col=brewer.pal(n=8, name="RdBu"),diag=FALSE, title = "SJ Corrplot", mar=c(0,0,1,0))

iq_train_labels %>% 
  dplyr::select(-city, -year, -weekofyear, -week_start_date) %>%
  cor(use = 'pairwise.complete.obs') -> M2

corrplot(M2, type="lower", method="color",
           col=brewer.pal(n=8, name="RdBu"),diag=FALSE, title = "IQ Corrplot", mar=c(0,0,1,0))

# see the correlations as barplot
sort(M1[21,-21]) %>%  
  as.data.frame %>% 
  `names<-`('correlation') %>%
  ggplot(aes(x = reorder(row.names(.), -correlation), y = correlation, fill = correlation)) + 
  geom_bar(stat='identity', colour = 'black') + scale_fill_continuous(guide = FALSE) + scale_y_continuous(limits =  c(-.15,.25)) +
  labs(title = 'San Jose\n Correlations', x = NULL, y = NULL) + coord_flip() -> cor1

# can use ncol(M1) instead of 21 to generalize the code
sort(M2[21,-21]) %>%  
  as.data.frame %>% 
  `names<-`('correlation') %>%
  ggplot(aes(x = reorder(row.names(.), -correlation), y = correlation, fill = correlation)) + 
  geom_bar(stat='identity', colour = 'black') + scale_fill_continuous(guide = FALSE) + scale_y_continuous(limits =  c(-.15,.25)) +
  labs(title = 'Iquitos\n Correlations', x = NULL, y = NULL) + coord_flip() -> cor2

grid.arrange(cor1, cor2, nrow = 1)

rm(cor1, cor2, M1, M2)

Comparison of correlations with the other non-na dataframes

Correlation with na.omit

library(tidyverse)
library(corrplot)
library(RColorBrewer)
require(gridExtra)

sj_train_labels.naomit %>% 
  dplyr::select(-year, -weekofyear) %>%
  cor(use = 'pairwise.complete.obs') -> M1

corrplot(M1, type="lower", method="color",
           col=brewer.pal(n=8, name="RdBu"),diag=FALSE, title = "SJ Corrplot", mar=c(0,0,1,0))

iq_train_labels.naomit %>% 
  dplyr::select(-year, -weekofyear) %>%
  cor(use = 'pairwise.complete.obs') -> M2

corrplot(M2, type="lower", method="color",
           col=brewer.pal(n=8, name="RdBu"),diag=FALSE, title = "IQ Corrplot", mar=c(0,0,1,0))

# see the correlations as barplot
sort(M1[21,-21]) %>%  
  as.data.frame %>% 
  `names<-`('correlation') %>%
  ggplot(aes(x = reorder(row.names(.), -correlation), y = correlation, fill = correlation)) + 
  geom_bar(stat='identity', colour = 'black') + scale_fill_continuous(guide = FALSE) + scale_y_continuous(limits =  c(-.15,.3)) +
  labs(title = 'San Jose\n Correlations', x = NULL, y = NULL) + coord_flip() -> cor1

# can use ncol(M1) instead of 21 to generalize the code
sort(M2[21,-21]) %>%  
  as.data.frame %>% 
  `names<-`('correlation') %>%
  ggplot(aes(x = reorder(row.names(.), -correlation), y = correlation, fill = correlation)) + 
  geom_bar(stat='identity', colour = 'black') + scale_fill_continuous(guide = FALSE) + scale_y_continuous(limits =  c(-.15,.3)) +
  labs(title = 'Iquitos\n Correlations', x = NULL, y = NULL) + coord_flip() -> cor2

grid.arrange(cor1, cor2, nrow = 1)

rm(cor1, cor2, M1, M2)

Correlation with Last NA

library(tidyverse)
library(corrplot)
library(RColorBrewer)
require(gridExtra)

sj_train_labels.lastna %>% 
  dplyr::select(-year, -weekofyear) %>%
  cor(use = 'pairwise.complete.obs') -> M1

corrplot(M1, type="lower", method="color",
           col=brewer.pal(n=8, name="RdBu"),diag=FALSE, title = "SJ Corrplot", mar=c(0,0,1,0))

iq_train_labels.lastna %>% 
  dplyr::select(-year, -weekofyear) %>%
  cor(use = 'pairwise.complete.obs') -> M2

corrplot(M2, type="lower", method="color",
           col=brewer.pal(n=8, name="RdBu"),diag=FALSE, title = "IQ Corrplot", mar=c(0,0,1,0))

# see the correlations as barplot
sort(M1[21,-21]) %>%  
  as.data.frame %>% 
  `names<-`('correlation') %>%
  ggplot(aes(x = reorder(row.names(.), -correlation), y = correlation, fill = correlation)) + 
  geom_bar(stat='identity', colour = 'black') + scale_fill_continuous(guide = FALSE) + scale_y_continuous(limits =  c(-.15,.3)) +
  labs(title = 'San Jose\n Correlations', x = NULL, y = NULL) + coord_flip() -> cor1

# can use ncol(M1) instead of 21 to generalize the code
sort(M2[21,-21]) %>%  
  as.data.frame %>% 
  `names<-`('correlation') %>%
  ggplot(aes(x = reorder(row.names(.), -correlation), y = correlation, fill = correlation)) + 
  geom_bar(stat='identity', colour = 'black') + scale_fill_continuous(guide = FALSE) + scale_y_continuous(limits =  c(-.15,.3)) +
  labs(title = 'Iquitos\n Correlations', x = NULL, y = NULL) + coord_flip() -> cor2

grid.arrange(cor1, cor2, nrow = 1)

rm(cor1, cor2, M1, M2)

Conclusion about correlation USE LASTNA

Different methods of imputing missing values had no impact on correlation. Will stick with last.na as the final version.

Remove dataframes which will no longer be used

rm(sj_train_labels.naomit, iq_train_labels.naomit)

FEATURE SELECTION and DIMENTIONALITY REDUCTION

We will use various methods to see if we can find any features that need to be eliminated

Feature selection via CaretR (Remove redundant features)

CaretR (Remove redundant features) for SJ

library(mlbench)
## Warning: package 'mlbench' was built under R version 3.4.4
library(caret)
# calculate correlation matrix
CorrelationMatrix <- cor(sj_train_labels.lastna)
# find attributes that are highly corrected (ideally >0.75)
highlyCorrelated <- findCorrelation(CorrelationMatrix, cutoff=0.75)
# print indexes of highly correlated attributes
print(highlyCorrelated)
## [1] 16 10  8  9 18 11 12  7  5
cnames <- colnames(sj_train_labels.lastna)
for (i in list(highlyCorrelated)){
  print(cnames[i])
}
## [1] "reanalysis_specific_humidity_g_per_kg"
## [2] "reanalysis_dew_point_temp_k"          
## [3] "reanalysis_air_temp_k"                
## [4] "reanalysis_avg_temp_k"                
## [5] "station_avg_temp_c"                   
## [6] "reanalysis_max_air_temp_k"            
## [7] "reanalysis_min_air_temp_k"            
## [8] "precipitation_amt_mm"                 
## [9] "ndvi_se"
rm(CorrelationMatrix, cnames, highlyCorrelated, i)

CaretR (Remove redundant features) for IQ

library(mlbench)
library(caret)
# calculate correlation matrix
CorrelationMatrix <- cor(iq_train_labels.lastna)
# find attributes that are highly corrected (ideally >0.75)
highlyCorrelated <- findCorrelation(CorrelationMatrix, cutoff=0.75)
# print indexes of highly correlated attributes
print(highlyCorrelated)
## [1] 17 11 16 10  9  7  5  3  4
cnames <- colnames(iq_train_labels.lastna)
for (i in list(highlyCorrelated)){
  print(cnames[i])
}
## [1] "reanalysis_tdtr_k"                    
## [2] "reanalysis_max_air_temp_k"            
## [3] "reanalysis_specific_humidity_g_per_kg"
## [4] "reanalysis_dew_point_temp_k"          
## [5] "reanalysis_avg_temp_k"                
## [6] "precipitation_amt_mm"                 
## [7] "ndvi_se"                              
## [8] "ndvi_ne"                              
## [9] "ndvi_nw"
rm(CorrelationMatrix, cnames, highlyCorrelated, i)

Feature selection via CaretR (via RFE)

CaretR (via RFE) for SJ

library(mlbench)
library(caret)
# define the control using a random forest selection function
control <- rfeControl(functions=rfFuncs, method="cv", number=10)
# run the RFE algorithm for SJ
sj_rfe_results <- rfe(sj_train_labels.lastna[,3:23], sj_train_labels.lastna$total_cases, sizes=c(3:23), rfeControl=control)
# summarize the results
print(sj_rfe_results)
## 
## Recursive feature selection
## 
## Outer resampling method: Cross-Validated (10 fold) 
## 
## Resampling performance over subset size:
## 
##  Variables   RMSE Rsquared   MAE RMSESD RsquaredSD  MAESD Selected
##          3 10.456   0.9671 3.744  2.888    0.01819 0.3535         
##          4 13.275   0.9512 5.886  3.035    0.02623 0.3356         
##          5 15.613   0.9388 7.923  3.136    0.05362 0.9229         
##          6  9.117   0.9782 3.783  2.577    0.01875 0.5087        *
##          7 11.306   0.9688 5.023  3.014    0.02683 0.7174         
##          8 12.513   0.9637 5.998  2.868    0.02517 0.7015         
##          9  9.922   0.9769 4.107  2.938    0.01826 0.5327         
##         10 10.974   0.9724 4.766  3.118    0.02021 0.4794         
##         11 12.205   0.9685 5.601  3.203    0.02107 0.6826         
##         12  9.824   0.9780 4.212  2.838    0.01404 0.5260         
##         13 10.769   0.9745 4.749  3.232    0.01629 0.6554         
##         14 12.055   0.9697 5.497  3.248    0.01878 0.6783         
##         15  9.954   0.9766 4.221  3.220    0.01495 0.5158         
##         16 11.254   0.9710 4.927  3.161    0.01948 0.4987         
##         17 12.030   0.9685 5.350  3.530    0.02089 0.6117         
##         18 10.314   0.9774 4.410  3.500    0.01403 0.6355         
##         19 10.969   0.9742 4.774  3.545    0.01640 0.6270         
##         20 11.818   0.9709 5.147  3.843    0.01679 0.7250         
##         21 10.570   0.9759 4.495  3.487    0.01426 0.5856         
## 
## The top 5 variables (out of 6):
##    total_cases, ndvi_se, ndvi_sw, reanalysis_specific_humidity_g_per_kg, reanalysis_dew_point_temp_k
# list the chosen features
predictors(sj_rfe_results)
## [1] "total_cases"                          
## [2] "ndvi_se"                              
## [3] "ndvi_sw"                              
## [4] "reanalysis_specific_humidity_g_per_kg"
## [5] "reanalysis_dew_point_temp_k"          
## [6] "ndvi_nw"
# plot the results
plot(sj_rfe_results, type=c("g", "o"), main = "RFE plot for SJ")

rm(sj_rfe_results, control)

CaretR (via RFE) for IQ

library(mlbench)
library(caret)

# define the control using a random forest selection function
control <- rfeControl(functions=rfFuncs, method="cv", number=10)
# run the RFE algorithm for IQ
iq_rfe_results <- rfe(iq_train_labels.lastna, iq_train_labels.lastna$total_cases, sizes=c(3:23), rfeControl=control)
# summarize the results
print(iq_rfe_results)
## 
## Recursive feature selection
## 
## Outer resampling method: Cross-Validated (10 fold) 
## 
## Resampling performance over subset size:
## 
##  Variables  RMSE Rsquared   MAE RMSESD RsquaredSD  MAESD Selected
##          3 2.572   0.9668 1.064  2.258    0.02849 0.4272        *
##          4 3.267   0.9481 1.500  2.324    0.03449 0.4719         
##          5 3.864   0.9228 1.922  2.348    0.05307 0.4705         
##          6 2.968   0.9496 1.115  2.562    0.04328 0.4235         
##          7 3.440   0.9352 1.407  2.835    0.05130 0.5517         
##          8 3.862   0.9197 1.700  2.868    0.05101 0.5601         
##          9 3.243   0.9406 1.219  2.857    0.04988 0.5310         
##         10 3.448   0.9330 1.376  2.849    0.04961 0.5315         
##         11 3.756   0.9200 1.563  2.950    0.05671 0.5472         
##         12 3.358   0.9341 1.259  2.849    0.05176 0.5190         
##         13 3.610   0.9260 1.419  2.927    0.05431 0.5456         
##         14 3.749   0.9192 1.548  2.979    0.05868 0.5545         
##         15 3.364   0.9335 1.303  2.943    0.05677 0.5229         
##         16 3.552   0.9271 1.409  2.969    0.05831 0.5624         
##         17 3.790   0.9167 1.551  2.960    0.05808 0.5378         
##         18 3.463   0.9297 1.332  2.890    0.05302 0.5435         
##         19 3.587   0.9265 1.445  2.878    0.05290 0.5480         
##         20 3.707   0.9214 1.536  2.973    0.05732 0.5758         
##         21 3.466   0.9290 1.351  2.893    0.05609 0.5216         
##         22 3.577   0.9247 1.433  2.976    0.05923 0.5575         
##         23 3.646   0.9254 1.502  3.003    0.05848 0.5809         
## 
## The top 3 variables (out of 3):
##    total_cases, year, station_avg_temp_c
# list the chosen features
 predictors(iq_rfe_results)
## [1] "total_cases"        "year"               "station_avg_temp_c"
# plot the results
plot(iq_rfe_results, type=c("g", "o"), main = "RFE plot for IQ")

rm(iq_rfe_results, control)

Feature selection using importance

Importance for SJ

# ensure results are repeatable
set.seed(136)

# load the library
library(mlbench)
library(caret)

# prepare training scheme
control <- trainControl(method="repeatedcv", number=11, repeats=1)

# train the model
model <- train(total_cases~., data=sj_train_labels.lastna[,3:23], method="cforest", preProcess="scale", trControl=control)

# estimate variable importance
importance <- varImp(model, scale=FALSE)
# summarize importance
print(importance)
## cforest variable importance
## 
##                                        Overall
## ndvi_se                               2707.068
## ndvi_sw                               1264.511
## reanalysis_specific_humidity_g_per_kg  524.414
## reanalysis_max_air_temp_k              232.114
## reanalysis_tdtr_k                       68.758
## station_avg_temp_c                      47.346
## station_max_temp_c                      38.592
## reanalysis_precip_amt_kg_per_m2         35.714
## ndvi_nw                                 31.800
## reanalysis_relative_humidity_percent    26.746
## station_min_temp_c                      25.271
## ndvi_ne                                 22.196
## reanalysis_dew_point_temp_k             17.165
## station_diur_temp_rng_c                 14.770
## reanalysis_min_air_temp_k               11.496
## precipitation_amt_mm                    10.432
## station_precip_mm                        6.900
## reanalysis_air_temp_k                    4.880
## reanalysis_avg_temp_k                    1.967
## reanalysis_sat_precip_amt_mm             0.000
# plot importance
plot(importance)

model$finalModel
## 
##   Random Forest using Conditional Inference Trees
## 
## Number of trees:  500 
## 
## Response:  .outcome 
## Inputs:  ndvi_ne, ndvi_nw, ndvi_se, ndvi_sw, precipitation_amt_mm, reanalysis_air_temp_k, reanalysis_avg_temp_k, reanalysis_dew_point_temp_k, reanalysis_max_air_temp_k, reanalysis_min_air_temp_k, reanalysis_precip_amt_kg_per_m2, reanalysis_relative_humidity_percent, reanalysis_sat_precip_amt_mm, reanalysis_specific_humidity_g_per_kg, reanalysis_tdtr_k, station_avg_temp_c, station_diur_temp_rng_c, station_max_temp_c, station_min_temp_c, station_precip_mm 
## Number of observations:  936
rm(model, importance, control, GCtorture)

Importance for IQ

# ensure results are repeatable
set.seed(136)

# load the library
library(mlbench)
library(caret)

# prepare training scheme
control <- trainControl(method="repeatedcv", number=10, repeats=3)

# train the model
model <- train(total_cases~., data=iq_train_labels.lastna[,3:23], method="cforest", preProcess="scale", trControl=control)

# estimate variable importance
importance <- varImp(model, scale=FALSE)
# summarize importance
print(importance)
## cforest variable importance
## 
##                                         Overall
## reanalysis_specific_humidity_g_per_kg  3.318451
## reanalysis_dew_point_temp_k            2.940082
## reanalysis_min_air_temp_k              1.674939
## reanalysis_tdtr_k                      1.378423
## reanalysis_precip_amt_kg_per_m2        1.285543
## station_min_temp_c                     1.212590
## reanalysis_relative_humidity_percent   1.087495
## reanalysis_avg_temp_k                  1.078973
## reanalysis_air_temp_k                  0.908769
## station_avg_temp_c                     0.842579
## station_max_temp_c                     0.559030
## station_diur_temp_rng_c                0.129754
## reanalysis_sat_precip_amt_mm           0.115857
## station_precip_mm                      0.047493
## ndvi_nw                                0.012152
## ndvi_sw                                0.007328
## ndvi_se                               -0.037845
## reanalysis_max_air_temp_k             -0.095271
## precipitation_amt_mm                  -0.168698
## ndvi_ne                               -0.267532
# plot importance
plot(importance)

rm(model, importance, control, GCtorture)
## Warning in rm(model, importance, control, GCtorture): object 'GCtorture'
## not found

Feature selection via Boruta

Boruta for SJ

library(Boruta)
## Warning: package 'Boruta' was built under R version 3.4.4
## Loading required package: ranger
## Warning: package 'ranger' was built under R version 3.4.4
sj_train_labels.boruta <- Boruta(sj_train_labels.lastna$total_cases~., data = sj_train_labels.lastna, doTrace = 2)
##  1. run of importance source...
##  2. run of importance source...
##  3. run of importance source...
##  4. run of importance source...
##  5. run of importance source...
##  6. run of importance source...
##  7. run of importance source...
##  8. run of importance source...
##  9. run of importance source...
##  10. run of importance source...
##  11. run of importance source...
##  12. run of importance source...
## After 12 iterations, +30 secs:
##  confirmed 14 attributes: ndvi_ne, ndvi_nw, ndvi_se, ndvi_sw, reanalysis_avg_temp_k and 9 more;
##  still have 8 attributes left.
##  13. run of importance source...
##  14. run of importance source...
##  15. run of importance source...
##  16. run of importance source...
## After 16 iterations, +39 secs:
##  confirmed 2 attributes: reanalysis_air_temp_k, station_min_temp_c;
##  still have 6 attributes left.
##  17. run of importance source...
##  18. run of importance source...
##  19. run of importance source...
## After 19 iterations, +46 secs:
##  confirmed 1 attribute: station_max_temp_c;
##  still have 5 attributes left.
##  20. run of importance source...
##  21. run of importance source...
##  22. run of importance source...
##  23. run of importance source...
##  24. run of importance source...
##  25. run of importance source...
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##  52. run of importance source...
##  53. run of importance source...
##  54. run of importance source...
##  55. run of importance source...
##  56. run of importance source...
## After 56 iterations, +2.2 mins:
##  confirmed 1 attribute: station_precip_mm;
##  still have 4 attributes left.
##  57. run of importance source...
##  58. run of importance source...
##  59. run of importance source...
##  60. run of importance source...
##  61. run of importance source...
##  62. run of importance source...
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##  74. run of importance source...
##  75. run of importance source...
##  76. run of importance source...
##  77. run of importance source...
##  78. run of importance source...
## After 78 iterations, +3 mins:
##  confirmed 1 attribute: reanalysis_sat_precip_amt_mm;
##  still have 3 attributes left.
##  79. run of importance source...
##  80. run of importance source...
##  81. run of importance source...
##  82. run of importance source...
##  83. run of importance source...
##  84. run of importance source...
##  85. run of importance source...
##  86. run of importance source...
##  87. run of importance source...
##  88. run of importance source...
## After 88 iterations, +3.4 mins:
##  confirmed 1 attribute: precipitation_amt_mm;
##  still have 2 attributes left.
##  89. run of importance source...
##  90. run of importance source...
##  91. run of importance source...
## After 91 iterations, +3.5 mins:
##  confirmed 1 attribute: station_diur_temp_rng_c;
##  still have 1 attribute left.
##  92. run of importance source...
##  93. run of importance source...
##  94. run of importance source...
##  95. run of importance source...
##  96. run of importance source...
##  97. run of importance source...
##  98. run of importance source...
##  99. run of importance source...
print(sj_train_labels.boruta)
## Boruta performed 99 iterations in 3.797255 mins.
##  21 attributes confirmed important: ndvi_ne, ndvi_nw, ndvi_se,
## ndvi_sw, precipitation_amt_mm and 16 more;
##  No attributes deemed unimportant.
##  1 tentative attributes left: reanalysis_tdtr_k;
#Fix and tentative attributes
sj_train_labels.boruta  <- TentativeRoughFix(sj_train_labels.boruta)
print(sj_train_labels.boruta)
## Boruta performed 99 iterations in 3.797255 mins.
## Tentatives roughfixed over the last 99 iterations.
##  22 attributes confirmed important: ndvi_ne, ndvi_nw, ndvi_se,
## ndvi_sw, precipitation_amt_mm and 17 more;
##  No attributes deemed unimportant.
#Boruta plot for SJ
plot(sj_train_labels.boruta, xlab = "", xaxt = "n")
lz<-lapply(1:ncol(sj_train_labels.boruta$ImpHistory),function(i)
sj_train_labels.boruta$ImpHistory[is.finite(sj_train_labels.boruta$ImpHistory[,i]),i])
names(lz) <- colnames(sj_train_labels.boruta$ImpHistory)
Labels <- sort(sapply(lz,median))
axis(side = 1,las=2,labels = names(Labels),
at = 1:ncol(sj_train_labels.boruta$ImpHistory), cex.axis = 0.7)

rm(lz, Labels, sj_train_labels.boruta)

Boruta for IQ

library(Boruta)
iq_train_labels.boruta <- Boruta(iq_train_labels.lastna$total_cases~., data = iq_train_labels.lastna, doTrace = 2)
##  1. run of importance source...
##  2. run of importance source...
##  3. run of importance source...
##  4. run of importance source...
##  5. run of importance source...
##  6. run of importance source...
##  7. run of importance source...
##  8. run of importance source...
##  9. run of importance source...
##  10. run of importance source...
##  11. run of importance source...
##  12. run of importance source...
## After 12 iterations, +14 secs:
##  confirmed 7 attributes: reanalysis_dew_point_temp_k, reanalysis_min_air_temp_k, reanalysis_precip_amt_kg_per_m2, reanalysis_specific_humidity_g_per_kg, station_avg_temp_c and 2 more;
##  still have 15 attributes left.
##  13. run of importance source...
##  14. run of importance source...
##  15. run of importance source...
##  16. run of importance source...
## After 16 iterations, +19 secs:
##  confirmed 3 attributes: reanalysis_air_temp_k, reanalysis_relative_humidity_percent, station_max_temp_c;
##  rejected 2 attributes: ndvi_nw, ndvi_sw;
##  still have 10 attributes left.
##  17. run of importance source...
##  18. run of importance source...
##  19. run of importance source...
## After 19 iterations, +22 secs:
##  confirmed 1 attribute: reanalysis_avg_temp_k;
##  still have 9 attributes left.
##  20. run of importance source...
##  21. run of importance source...
##  22. run of importance source...
## After 22 iterations, +25 secs:
##  rejected 1 attribute: station_precip_mm;
##  still have 8 attributes left.
##  23. run of importance source...
##  24. run of importance source...
##  25. run of importance source...
##  26. run of importance source...
## After 26 iterations, +29 secs:
##  rejected 1 attribute: ndvi_ne;
##  still have 7 attributes left.
##  27. run of importance source...
##  28. run of importance source...
##  29. run of importance source...
## After 29 iterations, +32 secs:
##  confirmed 1 attribute: reanalysis_tdtr_k;
##  still have 6 attributes left.
##  30. run of importance source...
##  31. run of importance source...
##  32. run of importance source...
##  33. run of importance source...
##  34. run of importance source...
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##  50. run of importance source...
##  51. run of importance source...
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##  77. run of importance source...
##  78. run of importance source...
##  79. run of importance source...
##  80. run of importance source...
##  81. run of importance source...
## After 81 iterations, +1.3 mins:
##  rejected 1 attribute: ndvi_se;
##  still have 5 attributes left.
##  82. run of importance source...
##  83. run of importance source...
##  84. run of importance source...
##  85. run of importance source...
##  86. run of importance source...
##  87. run of importance source...
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##  95. run of importance source...
##  96. run of importance source...
##  97. run of importance source...
##  98. run of importance source...
##  99. run of importance source...
print(iq_train_labels.boruta)
## Boruta performed 99 iterations in 1.595119 mins.
##  12 attributes confirmed important: reanalysis_air_temp_k,
## reanalysis_avg_temp_k, reanalysis_dew_point_temp_k,
## reanalysis_min_air_temp_k, reanalysis_precip_amt_kg_per_m2 and 7
## more;
##  5 attributes confirmed unimportant: ndvi_ne, ndvi_nw, ndvi_se,
## ndvi_sw, station_precip_mm;
##  5 tentative attributes left: precipitation_amt_mm,
## reanalysis_max_air_temp_k, reanalysis_sat_precip_amt_mm,
## station_diur_temp_rng_c, station_min_temp_c;
#Fix and tentative attributes
iq_train_labels.boruta  <- TentativeRoughFix(iq_train_labels.boruta)
print(iq_train_labels.boruta)
## Boruta performed 99 iterations in 1.595119 mins.
## Tentatives roughfixed over the last 99 iterations.
##  14 attributes confirmed important: precipitation_amt_mm,
## reanalysis_air_temp_k, reanalysis_avg_temp_k,
## reanalysis_dew_point_temp_k, reanalysis_min_air_temp_k and 9 more;
##  8 attributes confirmed unimportant: ndvi_ne, ndvi_nw, ndvi_se,
## ndvi_sw, reanalysis_max_air_temp_k and 3 more;
#Boruta plot for IQ

plot(iq_train_labels.boruta, xlab = "", xaxt = "n")
lz<-lapply(1:ncol(iq_train_labels.boruta$ImpHistory),function(i)
iq_train_labels.boruta$ImpHistory[is.finite(iq_train_labels.boruta$ImpHistory[,i]),i])
names(lz) <- colnames(iq_train_labels.boruta$ImpHistory)
Labels <- sort(sapply(lz,median))
axis(side = 1,las=2,labels = names(Labels),
at = 1:ncol(iq_train_labels.boruta$ImpHistory), cex.axis = 0.7)

rm(lz, Labels, iq_train_labels.boruta)

Feature Selection using Random Forest

Random Forest for SJ

library(reshape2)
## Warning: package 'reshape2' was built under R version 3.4.4
## 
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
## 
##     smiths
library(ggplot2)
library(randomForest)
## Warning: package 'randomForest' was built under R version 3.4.4
## randomForest 4.6-14
## Type rfNews() to see new features/changes/bug fixes.
## 
## Attaching package: 'randomForest'
## The following object is masked from 'package:ranger':
## 
##     importance
## The following object is masked from 'package:gridExtra':
## 
##     combine
## The following object is masked from 'package:dplyr':
## 
##     combine
## The following object is masked from 'package:ggplot2':
## 
##     margin
## The following object is masked from 'package:psych':
## 
##     outlier
library(caret)

#Fit a model
 
model_sj.rf <- randomForest(sj_train_labels.startweek$total_cases ~ 
          ndvi_ne +
            ndvi_nw +
            ndvi_se +
            ndvi_sw +
            precipitation_amt_mm +
            reanalysis_air_temp_k +
            reanalysis_avg_temp_k + 
            reanalysis_dew_point_temp_k +
            reanalysis_max_air_temp_k +
            reanalysis_min_air_temp_k +
            reanalysis_precip_amt_kg_per_m2 +
            reanalysis_relative_humidity_percent +
            reanalysis_sat_precip_amt_mm +
            reanalysis_specific_humidity_g_per_kg +
            reanalysis_tdtr_k + station_avg_temp_c +
            station_diur_temp_rng_c +
            station_max_temp_c +
            station_min_temp_c +
            station_precip_mm,
          sj_train_labels.startweek,
          importance = TRUE, 
          ntree=1000)
 
#How many trees are needed to reach the minimum error estimate? 
which.min(model_sj.rf$mse)
## [1] 665
plot(model_sj.rf) 

#Find the importance of the RF model
imp <- as.data.frame(sort(importance(model_sj.rf)[,1],decreasing = TRUE),optional = T)
names(imp) <- "% Inc MSE"
imp
##                                       % Inc MSE
## ndvi_se                               37.701045
## ndvi_sw                               23.312328
## reanalysis_dew_point_temp_k           13.287574
## reanalysis_specific_humidity_g_per_kg 12.960603
## ndvi_nw                               12.753006
## ndvi_ne                               12.341615
## station_precip_mm                     12.207094
## reanalysis_min_air_temp_k             12.109047
## reanalysis_relative_humidity_percent  10.933707
## reanalysis_precip_amt_kg_per_m2       10.373380
## reanalysis_tdtr_k                      8.822743
## reanalysis_max_air_temp_k              8.705044
## station_avg_temp_c                     7.945887
## reanalysis_air_temp_k                  7.590120
## reanalysis_sat_precip_amt_mm           7.526884
## station_min_temp_c                     7.114981
## reanalysis_avg_temp_k                  6.957742
## station_max_temp_c                     5.571364
## precipitation_amt_mm                   5.289672
## station_diur_temp_rng_c                4.998066
#graph the importance
varImpPlot(model_sj.rf, type = 1)

varImpPlot(model_sj.rf, type = 2)

rm(model_sj.rf, imp)

Random Forest for IQ

library(reshape2)
library(ggplot2)
library(randomForest)
library(caret)

#Fit a model
 
model_iq.rf <- randomForest(iq_train_labels.startweek$total_cases ~ 
          ndvi_ne +
            ndvi_nw +
            ndvi_se +
            ndvi_sw +
            precipitation_amt_mm +
            reanalysis_air_temp_k +
            reanalysis_avg_temp_k + 
            reanalysis_dew_point_temp_k +
            reanalysis_max_air_temp_k +
            reanalysis_min_air_temp_k +
            reanalysis_precip_amt_kg_per_m2 +
            reanalysis_relative_humidity_percent +
            reanalysis_sat_precip_amt_mm +
            reanalysis_specific_humidity_g_per_kg +
            reanalysis_tdtr_k + station_avg_temp_c +
            station_diur_temp_rng_c +
            station_max_temp_c +
            station_min_temp_c +
            station_precip_mm,
          iq_train_labels.startweek,
          importance = TRUE, 
          ntree=1000)
 
#How many trees are needed to reach the minimum error estimate? 
which.min(model_iq.rf$mse)
## [1] 880
plot(model_iq.rf) 

#Find the importance of the RF model
imp <- as.data.frame(sort(importance(model_iq.rf)[,1],decreasing = TRUE),optional = T)
names(imp) <- "% Inc MSE"
imp
##                                       % Inc MSE
## reanalysis_specific_humidity_g_per_kg 14.598346
## station_avg_temp_c                    13.816486
## reanalysis_precip_amt_kg_per_m2       12.857407
## reanalysis_dew_point_temp_k           12.609141
## station_max_temp_c                    12.320637
## reanalysis_relative_humidity_percent  10.737072
## reanalysis_tdtr_k                      9.481949
## reanalysis_min_air_temp_k              8.021002
## station_diur_temp_rng_c                7.831725
## reanalysis_max_air_temp_k              7.098665
## reanalysis_air_temp_k                  7.072691
## reanalysis_avg_temp_k                  6.403049
## ndvi_ne                                5.636981
## ndvi_sw                                5.358412
## reanalysis_sat_precip_amt_mm           4.912512
## ndvi_se                                4.140040
## precipitation_amt_mm                   4.040661
## station_precip_mm                      2.886239
## station_min_temp_c                     2.712698
## ndvi_nw                                1.982185
#graph the importance
varImpPlot(model_iq.rf, type = 1)

varImpPlot(model_iq.rf, type = 2)

rm(model_iq.rf, imp)

PEAK MODEL ANALYSIS

Set up the peak analysis

Determine the highest total_cases per week in a year

#calculate the max value by year and sort by highest cases
max_cases.year <-sort(tapply(sj_train_labels.lastna$total_cases, sj_train_labels.lastna$year, max), decreasing = TRUE)

max_cases.year
## 1994 1998 2007 1991 1995 2005 1997 1992 1999 2001 1990 1993 2003 2000 2002 
##  461  329  170  169  154  137  112  104   77   75   71   46   41   38   38 
## 1996 2006 2004 2008 
##   35   33   27   15
#Determine which weeks are associated to which max year values
dname <- dimnames(max_cases.year)

for (i in 1:6) {
  max_cases.week <-
    sort(tapply(sj_train_labels.lastna$total_cases[sj_train_labels.lastna$year == as.numeric(dname[[1]][i])], sj_train_labels.lastna$weekofyear[sj_train_labels.lastna$year == as.numeric(dname[[1]][i])], max), decreasing = TRUE)

print(dname[[1]][i])
print(max_cases.week[1:15])
}
## [1] "1994"
##  41  40  45  39  42  46  47  44  43  38  48  37  49  36  35 
## 461 426 410 395 381 364 359 353 333 302 288 272 221 202 179 
## [1] "1998"
##  32  33  31  34  35  30  36  29  27  28  41  26  40  37  51 
## 329 263 256 220 204 191 181 150 128 127 127 102 102  99  89 
## [1] "2007"
##  40  41  37  38  42  39  35  34  33  32  36  43  44  45  30 
## 170 135 112 106 106 101  92  76  75  72  71  68  48  48  42 
## [1] "1991"
##  48  42  49  40  44  45  43  47  46  41  50  39  38  51  34 
## 169 142 141 140 140 140 129 129 127 116 108  92  89  78  76 
## [1] "1995"
##  52   1   2   3  43   4  36  44   5  42  46  48  37  39  38 
## 154  91  72  56  48  46  40  40  37  36  36  34  33  33  31 
## [1] "2005"
##  35  36  33  34  37  31  32  38  30  39  29  41  42  43  44 
## 137 131 126 119 112  83  82  82  75  73  56  55  55  53  46
rm(dname, i, max_cases.week, max_cases.year)

Create dataframe with peaks only

Five peaks will be isolated from each city with 5 weeks around each side of the max yearly value. A new dataframe will be made for use in mutual information and then for prediction.

Add a binary variable “peak” for logistic regression purposes (peak = 1)

sj.peak.1994 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 1994 & sj_train_labels.lastna$weekofyear <= 46 & sj_train_labels.lastna$weekofyear >= 36 ,]

sj.peak.1998 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 1998 & sj_train_labels.lastna$weekofyear <= 47 & sj_train_labels.lastna$weekofyear >= 37 ,]

sj.peak.2007 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 2007 & sj_train_labels.lastna$weekofyear <= 45 & sj_train_labels.lastna$weekofyear >= 35 ,]

sj.peak.1991 <- rbind(sj_train_labels.lastna[sj_train_labels.lastna$year == 1991 & sj_train_labels.lastna$weekofyear <= 52 & sj_train_labels.lastna$weekofyear >= 43 ,],
                      sj_train_labels.lastna[sj_train_labels.lastna$year == 1992 & sj_train_labels.lastna$weekofyear == 1 ,] )

sj.peak.2005 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 2005 & sj_train_labels.lastna$weekofyear <= 40 & sj_train_labels.lastna$weekofyear >= 30 ,]

sj.peak <-rbind(sj.peak.1991,sj.peak.1994,sj.peak.1998,sj.peak.2005,sj.peak.2007)

sj.peak$peak <- 1

rm(sj.peak.1991,sj.peak.1994,sj.peak.1998,sj.peak.2005,sj.peak.2007)

Create dataframe with non-peaks

Add a binary variable “nonpeak” for logistic regression purposes (peak = 0)

sj.nonpeak.1997 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 1997 & sj_train_labels.lastna$weekofyear <= 22 & sj_train_labels.lastna$weekofyear >= 12 ,]

sj.nonpeak.2001 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 2001 & sj_train_labels.lastna$weekofyear <= 22 & sj_train_labels.lastna$weekofyear >= 12 ,]

sj.nonpeak.2003 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 2003 & sj_train_labels.lastna$weekofyear <= 22 & sj_train_labels.lastna$weekofyear >= 12 ,]

sj.nonpeak.1993 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 1993 & sj_train_labels.lastna$weekofyear <= 22 & sj_train_labels.lastna$weekofyear >= 12 ,]

sj.nonpeak.1996 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 1996 & sj_train_labels.lastna$weekofyear <= 22 & sj_train_labels.lastna$weekofyear >= 12 ,]

sj.nonpeak <-rbind(sj.nonpeak.1997,
                   sj.nonpeak.2001,
                   sj.nonpeak.2003,
                   sj.nonpeak.1993,
                   sj.nonpeak.1996)

rm(sj.nonpeak.1997,sj.nonpeak.2001,sj.nonpeak.2003,sj.nonpeak.1993,sj.nonpeak.1996)

sj.nonpeak$peak <- 0

Feature selection using logistic regression

Build and test a logistic regression model for the peaks

sj_peak.glm <- rbind(sj.peak, sj.nonpeak)

# Fit a logistic regression model
fit_glm <- glm(sj_peak.glm$peak ~ .,
               sj_peak.glm,
               family = "binomial")
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
# generate summary
 
summary(fit_glm)
## 
## Call:
## glm(formula = sj_peak.glm$peak ~ ., family = "binomial", data = sj_peak.glm)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -1.661e-05  -2.110e-08   0.000e+00   2.110e-08   1.585e-05  
## 
## Coefficients: (1 not defined because of singularities)
##                                         Estimate Std. Error z value
## (Intercept)                            8.124e+03  7.892e+07       0
## year                                  -1.285e+00  2.564e+04       0
## weekofyear                             5.992e-01  1.313e+04       0
## ndvi_ne                                5.914e+01  1.025e+06       0
## ndvi_nw                               -3.671e+01  1.408e+06       0
## ndvi_se                                2.318e+02  3.178e+06       0
## ndvi_sw                               -2.042e+02  3.702e+06       0
## precipitation_amt_mm                  -1.663e-01  1.923e+03       0
## reanalysis_air_temp_k                 -1.604e+02  2.583e+06       0
## reanalysis_avg_temp_k                 -4.632e+01  9.555e+05       0
## reanalysis_dew_point_temp_k            1.302e+02  2.787e+06       0
## reanalysis_max_air_temp_k             -1.354e+01  1.593e+05       0
## reanalysis_min_air_temp_k             -4.127e+00  2.013e+05       0
## reanalysis_precip_amt_kg_per_m2        2.574e-01  2.709e+03       0
## reanalysis_relative_humidity_percent  -5.288e+01  5.464e+05       0
## reanalysis_sat_precip_amt_mm                  NA         NA      NA
## reanalysis_specific_humidity_g_per_kg  1.277e+02  1.006e+06       0
## reanalysis_tdtr_k                      4.768e+00  1.680e+05       0
## station_avg_temp_c                    -1.567e+01  3.547e+05       0
## station_diur_temp_rng_c               -1.341e+01  1.116e+05       0
## station_max_temp_c                     8.618e+00  1.346e+05       0
## station_min_temp_c                    -1.397e+01  1.565e+05       0
## station_precip_mm                     -1.428e-01  2.519e+03       0
## total_cases                            2.963e-01  1.900e+03       0
##                                       Pr(>|z|)
## (Intercept)                                  1
## year                                         1
## weekofyear                                   1
## ndvi_ne                                      1
## ndvi_nw                                      1
## ndvi_se                                      1
## ndvi_sw                                      1
## precipitation_amt_mm                         1
## reanalysis_air_temp_k                        1
## reanalysis_avg_temp_k                        1
## reanalysis_dew_point_temp_k                  1
## reanalysis_max_air_temp_k                    1
## reanalysis_min_air_temp_k                    1
## reanalysis_precip_amt_kg_per_m2              1
## reanalysis_relative_humidity_percent         1
## reanalysis_sat_precip_amt_mm                NA
## reanalysis_specific_humidity_g_per_kg        1
## reanalysis_tdtr_k                            1
## station_avg_temp_c                           1
## station_diur_temp_rng_c                      1
## station_max_temp_c                           1
## station_min_temp_c                           1
## station_precip_mm                            1
## total_cases                                  1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1.5249e+02  on 109  degrees of freedom
## Residual deviance: 1.7801e-09  on  87  degrees of freedom
## AIC: 46
## 
## Number of Fisher Scoring iterations: 25
rm(sj_peak.glm, fit_glm)

Feature selection using Mutual Information

This analysis looks at the mutual information for a peak prediction model. Five peaks will be used

Determine mutual info for the new dataframe with peaks

library(entropy)
library(infotheo)
## 
## Attaching package: 'infotheo'
## The following objects are masked from 'package:entropy':
## 
##     discretize, entropy
mu <- data.frame()

cnames <- colnames(sj.peak)
for (i in 5:(ncol(sj.peak)-2)) {
  disc1 <- discretize(sj.peak$total_cases)
  disc2 <- discretize(sj.peak[,i])
  mu[i-4,1] <- cnames[i]
  mu[i-4,2] <- mutinformation(disc1, disc2)
}
mu[order(mu$V2, decreasing = TRUE),]
##                                       V1         V2
## 1                                ndvi_se 0.22008822
## 15               station_diur_temp_rng_c 0.10178915
## 6            reanalysis_dew_point_temp_k 0.08947514
## 12 reanalysis_specific_humidity_g_per_kg 0.07098685
## 7              reanalysis_max_air_temp_k 0.06446396
## 18                     station_precip_mm 0.05977442
## 8              reanalysis_min_air_temp_k 0.05513424
## 4                  reanalysis_air_temp_k 0.05412362
## 17                    station_min_temp_c 0.05227911
## 5                  reanalysis_avg_temp_k 0.04437999
## 13                     reanalysis_tdtr_k 0.04282410
## 10  reanalysis_relative_humidity_percent 0.03870491
## 9        reanalysis_precip_amt_kg_per_m2 0.03184109
## 16                    station_max_temp_c 0.03036126
## 2                                ndvi_sw 0.02205445
## 14                    station_avg_temp_c 0.02192181
## 3                   precipitation_amt_mm 0.01964312
## 11          reanalysis_sat_precip_amt_mm 0.01964312
rm(mu, cnames, i, disc1, disc2)

Determine mutual info for the new dataframe with nonpeaks

library(entropy)
library(infotheo)

mu <- data.frame()

cnames <- colnames(sj.nonpeak)
for (i in 5:(ncol(sj.nonpeak)-2)) {
  disc1 <- discretize(sj.nonpeak$total_cases)
  disc2 <- discretize(sj.nonpeak[,i])
  mu[i-4,1] <- cnames[i]
  mu[i-4,2] <- mutinformation(disc1, disc2)
}
mu[order(mu$V2, decreasing = TRUE),]
##                                       V1         V2
## 1                                ndvi_se 0.09648317
## 5                  reanalysis_avg_temp_k 0.06407748
## 4                  reanalysis_air_temp_k 0.06179880
## 2                                ndvi_sw 0.05977442
## 9        reanalysis_precip_amt_kg_per_m2 0.04481389
## 8              reanalysis_min_air_temp_k 0.04417511
## 10  reanalysis_relative_humidity_percent 0.04358990
## 18                     station_precip_mm 0.03305411
## 17                    station_min_temp_c 0.02549627
## 14                    station_avg_temp_c 0.02374231
## 13                     reanalysis_tdtr_k 0.02148998
## 7              reanalysis_max_air_temp_k 0.01964312
## 15               station_diur_temp_rng_c 0.01804743
## 3                   precipitation_amt_mm 0.01703681
## 11          reanalysis_sat_precip_amt_mm 0.01703681
## 16                    station_max_temp_c 0.01473814
## 12 reanalysis_specific_humidity_g_per_kg 0.01138601
## 6            reanalysis_dew_point_temp_k 0.01076950
rm(mu, cnames, i, disc1, disc2)

Binary Classification

Naive Bayes model for the peak model

library(e1071)
## Warning: package 'e1071' was built under R version 3.4.4
sj_peak.nb <- rbind(sj.peak, sj.nonpeak)

# Fit a Naive Bayes model
fit_nb <- naiveBayes(sj_peak.nb$total_cases ~ ., sj_peak.nb)
 
# generate summary

summary(fit_nb)
##         Length Class  Mode   
## apriori 62     table  numeric
## tables  23     -none- list   
## levels   0     -none- NULL   
## call     4     -none- call
#fit_nb

#remove fit_nb
rm(fit_nb, sj_peak.nb)

Decision Tree model for the peak model

library(party)
## Warning: package 'party' was built under R version 3.4.4
## Loading required package: grid
## Loading required package: mvtnorm
## Loading required package: modeltools
## Loading required package: stats4
## 
## Attaching package: 'modeltools'
## The following object is masked from 'package:plyr':
## 
##     empty
## Loading required package: strucchange
## Warning: package 'strucchange' was built under R version 3.4.4
## Loading required package: sandwich
## 
## Attaching package: 'strucchange'
## The following object is masked from 'package:stringr':
## 
##     boundary
sj_peak.tree <- rbind(sj.peak, sj.nonpeak)

# Fit a logistic regression model
fit_tree <- ctree(sj_peak.tree$peak ~ .,
               sj_peak.tree)
 
# generate summary
 
summary(fit_tree)
##     Length      Class       Mode 
##          1 BinaryTree         S4
fit_tree
## 
##   Conditional inference tree with 3 terminal nodes
## 
## Response:  sj_peak.tree$peak 
## Inputs:  year, weekofyear, ndvi_ne, ndvi_nw, ndvi_se, ndvi_sw, precipitation_amt_mm, reanalysis_air_temp_k, reanalysis_avg_temp_k, reanalysis_dew_point_temp_k, reanalysis_max_air_temp_k, reanalysis_min_air_temp_k, reanalysis_precip_amt_kg_per_m2, reanalysis_relative_humidity_percent, reanalysis_sat_precip_amt_mm, reanalysis_specific_humidity_g_per_kg, reanalysis_tdtr_k, station_avg_temp_c, station_diur_temp_rng_c, station_max_temp_c, station_min_temp_c, station_precip_mm, total_cases 
## Number of observations:  110 
## 
## 1) weekofyear <= 22; criterion = 1, statistic = 88.318
##   2) total_cases <= 13; criterion = 1, statistic = 46.419
##     3)*  weights = 49 
##   2) total_cases > 13
##     4)*  weights = 7 
## 1) weekofyear > 22
##   5)*  weights = 54
rm(sj_peak.tree, fit_tree)

Remove peak and non-peak dataframes

write.csv(rbind(sj.peak, sj.nonpeak), file = "peak.csv")
rm(sj.peak, sj.nonpeak)

PREDICTIVE MODELS

The following predictive models will reivew the Root Square Mean Error by each city. This is done without feature selection and with missing values imputed as the last non-na values.

80/20Training and validation set

SJ: training and validation

set.seed(136) 
 
# randomly pick 80% of the number of observations
index.sj <- sample(1:nrow(sj_train_labels.startweek),size = 0.8*nrow(sj_train_labels.startweek)) 
 
# subset train_labels to include only the elements in the index
train.sj <- sj_train_labels.startweek[index.sj,] 
 
# subset train_labels to include all but the elements in the index
validation.sj <- sj_train_labels.startweek[-index.sj,] 
 
nrow(train.sj)
## [1] 748
nrow(validation.sj)
## [1] 188
# # Create a dataframe with train and test indicator...
# group <- rep(NA,nrow(sj_train_labels.startweek))
# 
# group <- ifelse(seq(1,nrow(sj_train_labels.startweek)) %in% index,"Train","Validation")
# 
# df <- data.frame(date=sj_train_labels.startweek$week_start_date,cases=sj_train_labels.startweek$total_cases,group)
# 
# # ...and plot it
# ggplot(df,aes(x = date,y = cases, color = group)) + geom_point() +
#   scale_color_discrete(name="") + theme(legend.position="top")

rm(index.sj)

IQ: training and validation

set.seed(136) 
 
# randomly pick 80% of the number of observations
index.iq <- sample(1:nrow(iq_train_labels.startweek),size = 0.8*nrow(iq_train_labels.startweek)) 
 
# subset train_labels to include only the elements in the index
train.iq <- iq_train_labels.startweek[index.iq,] 
 
# subset train_labels to include all but the elements in the index
validation.iq <- iq_train_labels.startweek[-index.iq,] 
 
nrow(train.iq)
## [1] 416
nrow(validation.iq)
## [1] 104
rm(index.iq)

Baseline models

Baseline model 1 for SJ

The baseline model shifts the total_cases down by one so that the values fall down to the next week. The difference between the orignal and the shifted values are taken and the RMSE is used as the metric to measure performance.

#create a new data frame from the lastna dataframe
sj_train_labels.shift <- sj_train_labels.lastna

#Make a copy of the total_cases variable
sj_train_labels.shift$total_cases2 <- sj_train_labels.shift$total_cases

#shift the values down by one
sj_train_labels.shift['total_cases2'] <- c(NA, head(sj_train_labels.shift['total_cases2'], dim(sj_train_labels.shift)[1] - 1)[[1]])

#replace the first NA with zero
sj_train_labels.shift$total_cases2[1] <- 0

#take the difference between total_cases and total_cases2
sj_train_labels.shift$diff <- sj_train_labels.shift$total_cases2 - sj_train_labels.shift$total_cases

# Evaluate RMSE and MAE on the validation data
RMSE.SJ.baseline1 <- sqrt(mean((sj_train_labels.shift$diff)^2))
RMSE.SJ.baseline1
## [1] 15.34033
MAE.SJ.baseline1 <- mean(abs(sj_train_labels.shift$diff))
MAE.SJ.baseline1
## [1] 8.394231
rm(sj_train_labels.shift)

Baseline model 1 for IQ

The baseline model shifts the total_cases down by one so that the values fall down to the next week. The difference between the orignal and the shifted values are taken and the RMSE is used as the metric to measure performance.

#create a new data frame from the lastna dataframe
iq_train_labels.shift <- iq_train_labels.lastna

#Make a copy of the total_cases variable
iq_train_labels.shift$total_cases2 <- iq_train_labels.shift$total_cases

#shift the values down by one
iq_train_labels.shift['total_cases2'] <- c(NA, head(iq_train_labels.shift['total_cases2'], dim(iq_train_labels.shift)[1] - 1)[[1]])

#replace the first NA with zero
iq_train_labels.shift$total_cases2[1] <- 0

#take the difference between total_cases and total_cases2
iq_train_labels.shift$diff <- iq_train_labels.shift$total_cases2 - iq_train_labels.shift$total_cases

# Evaluate RMSE and MAE on the validation data
RMSE.IQ.baseline1 <- sqrt(mean((iq_train_labels.shift$diff)^2))
RMSE.IQ.baseline1
## [1] 7.660086
MAE.IQ.baseline1 <- mean(abs(iq_train_labels.shift$diff))
MAE.IQ.baseline1
## [1] 3.930769
rm(iq_train_labels.shift)

Baseline model 2 for SJ

#Here is a plot showing which points belong to which set (train or test).
library(ggplot2)

# Baseline model - predict the mean of the training data
best.guess.sj <- mean(train.sj$total_cases)
 
# Evaluate RMSE and MAE on the validation data
RMSE.SJ.baseline2 <- sqrt(mean((best.guess.sj-validation.sj$total_cases)^2))
RMSE.SJ.baseline2
## [1] 44.41748
MAE.SJ.baseline2 <- mean(abs(best.guess.sj-validation.sj$total_cases))
MAE.SJ.baseline2
## [1] 27.03268
rm(best.guess.sj)

Baseline model 2 for IQ

#Here is a plot showing which points belong to which set (train or test).

library(ggplot2)

# Baseline model - predict the mean of the training data
best.guess.iq <- mean(train.iq$total_cases)
 
# Evaluate RMSE and MAE on the validation data
RMSE.IQ.baseline2 <- sqrt(mean((best.guess.iq-validation.iq$total_cases)^2))
RMSE.IQ.baseline2
## [1] 11.6964
MAE.IQ.baseline2 <- mean(abs(best.guess.iq-validation.iq$total_cases))
MAE.IQ.baseline2
## [1] 7.383275
rm(best.guess.iq)

Negative binomial regression (NBR)

NBR for SJ

library(MASS)
## 
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
## 
##     select
library(reshape2)
library(ggplot2)

#determine the dispersion of the total_cases

round(with(sj_train_labels.startweek, mean(total_cases),2))
## [1] 34
round(with(sj_train_labels.startweek, var(total_cases),2))
## [1] 2640
#As there is over-dispersion of total_cases (variance is greater than the mean) we can go ahead and build the NBR model 

#Build the model
model_sj.nbr <- glm.nb(formula = total_cases ~ ., data = train.sj[,4:24])

summary(model_sj.nbr)
## 
## Call:
## glm.nb(formula = total_cases ~ ., data = train.sj[, 4:24], init.theta = 1.238752535, 
##     link = log)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6005  -1.0132  -0.4303   0.2511   3.7686  
## 
## Coefficients: (1 not defined because of singularities)
##                                         Estimate Std. Error z value
## (Intercept)                           -36.095981  35.906521  -1.005
## ndvi_ne                                -0.075816   0.439385  -0.173
## ndvi_nw                                 1.745488   0.506357   3.447
## ndvi_se                                -6.437402   0.998317  -6.448
## ndvi_sw                                 5.462943   1.033934   5.284
## precipitation_amt_mm                   -0.002296   0.001069  -2.148
## reanalysis_air_temp_k                   2.859593   1.787091   1.600
## reanalysis_avg_temp_k                  -0.882312   0.449115  -1.965
## reanalysis_dew_point_temp_k            -2.793170   1.662257  -1.680
## reanalysis_max_air_temp_k               0.301373   0.106441   2.831
## reanalysis_min_air_temp_k              -0.052397   0.112896  -0.464
## reanalysis_precip_amt_kg_per_m2         0.001093   0.001273   0.858
## reanalysis_relative_humidity_percent    0.400739   0.369438   1.085
## reanalysis_sat_precip_amt_mm                  NA         NA      NA
## reanalysis_specific_humidity_g_per_kg   1.028983   0.466649   2.205
## reanalysis_tdtr_k                      -0.608646   0.118727  -5.126
## station_avg_temp_c                     -0.222566   0.114877  -1.937
## station_diur_temp_rng_c                 0.055151   0.071591   0.770
## station_max_temp_c                      0.041047   0.057318   0.716
## station_min_temp_c                     -0.007992   0.070262  -0.114
## station_precip_mm                      -0.001965   0.001690  -1.163
##                                       Pr(>|z|)    
## (Intercept)                           0.314764    
## ndvi_ne                               0.863005    
## ndvi_nw                               0.000567 ***
## ndvi_se                               1.13e-10 ***
## ndvi_sw                               1.27e-07 ***
## precipitation_amt_mm                  0.031687 *  
## reanalysis_air_temp_k                 0.109568    
## reanalysis_avg_temp_k                 0.049465 *  
## reanalysis_dew_point_temp_k           0.092890 .  
## reanalysis_max_air_temp_k             0.004635 ** 
## reanalysis_min_air_temp_k             0.642565    
## reanalysis_precip_amt_kg_per_m2       0.390702    
## reanalysis_relative_humidity_percent  0.278043    
## reanalysis_sat_precip_amt_mm                NA    
## reanalysis_specific_humidity_g_per_kg 0.027451 *  
## reanalysis_tdtr_k                     2.95e-07 ***
## station_avg_temp_c                    0.052694 .  
## station_diur_temp_rng_c               0.441080    
## station_max_temp_c                    0.473911    
## station_min_temp_c                    0.909437    
## station_precip_mm                     0.244969    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Negative Binomial(1.2388) family taken to be 1)
## 
##     Null deviance: 1085.5  on 747  degrees of freedom
## Residual deviance:  827.3  on 728  degrees of freedom
## AIC: 6640.9
## 
## Number of Fisher Scoring iterations: 1
## 
## 
##               Theta:  1.2388 
##           Std. Err.:  0.0614 
## 
##  2 x log-likelihood:  -6598.8710
prediction_sj.nbr <-  predict(model_sj.nbr, validation.sj, type = 'response')
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
#Plot the prediction for NBR
df_prediction_sj.nbr <- data.frame('prediction' = prediction_sj.nbr,
                                   'actual' = validation.sj$total_cases,
                                   'time' = validation.sj$week_start_date)

df_prediction_sj.nbr <- melt(df_prediction_sj.nbr, id.vars = 'time')

ggplot(df_prediction_sj.nbr, aes(x = time, y = value, color = variable)) +
  geom_line() +
  ggtitle('NBR: Dengue predicted Cases vs. Actual Cases (City-San Juan) ')

# Evaluate RMSE and MAE on the validation data
RMSE.SJ.nbr <- sqrt(mean((prediction_sj.nbr-validation.sj$total_cases)^2))
RMSE.SJ.nbr
## [1] 42.50416
MAE.SJ.nbr <- mean(abs(prediction_sj.nbr-validation.sj$total_cases))
MAE.SJ.nbr
## [1] 26.15555
rm(df_prediction_sj.nbr, model_sj.nbr, prediction_sj.nbr)

NBR for IQ

library(MASS)
library(reshape2)
library(ggplot2)

#determine the dispersion of the total_cases

round(with(iq_train_labels.startweek, mean(total_cases),2))
## [1] 8
round(with(iq_train_labels.startweek, var(total_cases),2))
## [1] 116
#As there is over-dispersion of total_cases (variance is greater than the mean) we can go ahead and build the NBR model 

#Build the model
model_iq.nbr <- glm.nb(formula = total_cases ~ ., data = train.iq[,4:24])

summary(model_iq.nbr)
## 
## Call:
## glm.nb(formula = total_cases ~ ., data = train.iq[, 4:24], init.theta = 0.8927711016, 
##     link = log)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3298  -1.1448  -0.3802   0.3018   3.6056  
## 
## Coefficients: (1 not defined because of singularities)
##                                         Estimate Std. Error z value
## (Intercept)                           -3.9555345 14.4163166  -0.274
## ndvi_ne                                2.0225152  1.5012052   1.347
## ndvi_nw                                1.3923607  1.2471222   1.116
## ndvi_se                               -4.5143238  1.1676946  -3.866
## ndvi_sw                               -0.3853344  1.3088897  -0.294
## precipitation_amt_mm                  -0.0004742  0.0019193  -0.247
## reanalysis_air_temp_k                  0.3821218  0.6047721   0.632
## reanalysis_avg_temp_k                 -0.1713872  0.2741650  -0.625
## reanalysis_dew_point_temp_k           -1.4903126  0.8783992  -1.697
## reanalysis_max_air_temp_k             -0.0937201  0.0551876  -1.698
## reanalysis_min_air_temp_k              0.0119852  0.0798743   0.150
## reanalysis_precip_amt_kg_per_m2       -0.0023324  0.0014441  -1.615
## reanalysis_relative_humidity_percent   0.0330521  0.1314110   0.252
## reanalysis_sat_precip_amt_mm                  NA         NA      NA
## reanalysis_specific_humidity_g_per_kg  1.5909235  0.7529229   2.113
## reanalysis_tdtr_k                      0.0354126  0.0897535   0.395
## station_avg_temp_c                     0.0127747  0.1083638   0.118
## station_diur_temp_rng_c                0.0277937  0.0668043   0.416
## station_max_temp_c                     0.1462946  0.0704404   2.077
## station_min_temp_c                     0.0706209  0.0738444   0.956
## station_precip_mm                      0.0009194  0.0009481   0.970
##                                       Pr(>|z|)    
## (Intercept)                           0.783793    
## ndvi_ne                               0.177896    
## ndvi_nw                               0.264226    
## ndvi_se                               0.000111 ***
## ndvi_sw                               0.768454    
## precipitation_amt_mm                  0.804843    
## reanalysis_air_temp_k                 0.527489    
## reanalysis_avg_temp_k                 0.531889    
## reanalysis_dew_point_temp_k           0.089768 .  
## reanalysis_max_air_temp_k             0.089468 .  
## reanalysis_min_air_temp_k             0.880725    
## reanalysis_precip_amt_kg_per_m2       0.106282    
## reanalysis_relative_humidity_percent  0.801414    
## reanalysis_sat_precip_amt_mm                NA    
## reanalysis_specific_humidity_g_per_kg 0.034601 *  
## reanalysis_tdtr_k                     0.693172    
## station_avg_temp_c                    0.906157    
## station_diur_temp_rng_c               0.677376    
## station_max_temp_c                    0.037815 *  
## station_min_temp_c                    0.338897    
## station_precip_mm                     0.332217    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Negative Binomial(0.8928) family taken to be 1)
## 
##     Null deviance: 561.45  on 415  degrees of freedom
## Residual deviance: 474.29  on 396  degrees of freedom
## AIC: 2495.4
## 
## Number of Fisher Scoring iterations: 1
## 
## 
##               Theta:  0.8928 
##           Std. Err.:  0.0737 
## 
##  2 x log-likelihood:  -2453.4450
prediction_iq.nbr <-  predict(model_iq.nbr, validation.iq, type = 'response')
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
#Plot the prediction for NBR
df_prediction_iq.nbr <- data.frame('prediction' = prediction_iq.nbr,
                                   'actual' = validation.iq$total_cases,
                                   'time' = validation.iq$week_start_date)

df_prediction_iq.nbr <- melt(df_prediction_iq.nbr, id.vars = 'time')

ggplot(df_prediction_iq.nbr, aes(x = time, y = value, color = variable)) +
  geom_line() +
  ggtitle('NBR: Dengue predicted Cases vs. Actual Cases (City-IQUITOS) ')

# Evaluate RMSE and MAE on the validation data
RMSE.IQ.nbr <- sqrt(mean((prediction_iq.nbr-validation.iq$total_cases)^2))
RMSE.IQ.nbr
## [1] 11.934
MAE.IQ.nbr <- mean(abs(prediction_iq.nbr-validation.iq$total_cases))
MAE.IQ.nbr
## [1] 7.742887
rm(df_prediction_iq.nbr, model_iq.nbr, prediction_iq.nbr)

Support Vector Machines

SVM for SJ

library(kernlab)
## Warning: package 'kernlab' was built under R version 3.4.4
## 
## Attaching package: 'kernlab'
## The following object is masked from 'package:modeltools':
## 
##     prior
## The following object is masked from 'package:purrr':
## 
##     cross
## The following object is masked from 'package:ggplot2':
## 
##     alpha
## The following object is masked from 'package:psych':
## 
##     alpha
library(reshape2)
library(ggplot2)

#Build the model
model_sj.svm <- ksvm(total_cases ~  ., data = train.sj, kernel = "vanilladot")
##  Setting default kernel parameters
prediction_sj.svm <-  predict(model_sj.svm, validation.sj)

#Plot the prediction for NBR
df_prediction_sj.svm <- data.frame('prediction' = prediction_sj.svm,
                                   'actual' = validation.sj$total_cases,
                                   'time' = validation.sj$week_start_date)

df_prediction_sj.svm <- melt(df_prediction_sj.svm, id.vars = 'time')

ggplot(df_prediction_sj.svm, aes(x = time, y = value, color = variable)) +
  geom_line() +
  ggtitle('SVM: Dengue predicted Cases vs. Actual Cases (City-San Juan) ')

# Evaluate RMSE and MAE on the validation data
RMSE.SJ.svm <- sqrt(mean((prediction_sj.svm-validation.sj$total_cases)^2))
RMSE.SJ.svm
## [1] 42.23424
MAE.SJ.svm <- mean(abs(prediction_sj.svm-validation.sj$total_cases))
MAE.SJ.svm
## [1] 19.76721
rm(df_prediction_sj.svm, model_sj.svm, prediction_sj.svm)

SVM for IQ

library(kernlab)
library(reshape2)
library(ggplot2)

#Build the model
model_iq.svm <- ksvm(total_cases ~  ., data = train.iq, kernel = "vanilladot")
##  Setting default kernel parameters
prediction_iq.svm <-  predict(model_iq.svm, validation.iq)

#Plot the prediction for NBR
df_prediction_iq.svm <- data.frame('prediction' = prediction_iq.svm,
                                   'actual' = validation.iq$total_cases,
                                   'time' = validation.iq$week_start_date)

df_prediction_iq.svm <- melt(df_prediction_iq.svm, id.vars = 'time')

ggplot(df_prediction_iq.svm, aes(x = time, y = value, color = variable)) +
  geom_line() +
  ggtitle('SVM: Dengue predicted Cases vs. Actual Cases (City-Iquitos) ')

# Evaluate RMSE and MAE on the validation data
RMSE.IQ.svm <- sqrt(mean((prediction_iq.svm-validation.iq$total_cases)^2))
RMSE.IQ.svm
## [1] 11.95082
MAE.IQ.svm <- mean(abs(prediction_iq.svm-validation.iq$total_cases))
MAE.IQ.svm
## [1] 6.398152
rm(df_prediction_iq.svm, model_iq.svm, prediction_iq.svm)

Random Forest

RF without CV for SJ

library(reshape2)
library(ggplot2)
library(randomForest)
library(caret)

set.seed(136)

#Build the model
model_sj.rf <- randomForest(formula = total_cases ~ ., data = train.sj[,4:24])

model_sj.rf
## 
## Call:
##  randomForest(formula = total_cases ~ ., data = train.sj[, 4:24]) 
##                Type of random forest: regression
##                      Number of trees: 500
## No. of variables tried at each split: 6
## 
##           Mean of squared residuals: 1444.554
##                     % Var explained: 48.49
prediction_sj.rf <-  predict(model_sj.rf, validation.sj, type = 'response')

#Plot the prediction for NBR
df_prediction_sj.rf <- data.frame('prediction' = prediction_sj.rf,
                                   'actual' = validation.sj$total_cases,
                                   'time' = validation.sj$week_start_date)

df_prediction_sj.rf <- melt(df_prediction_sj.rf, id.vars = 'time')

ggplot(df_prediction_sj.rf, aes(x = time, y = value, color = variable)) +
  geom_line() +
  ggtitle('RF: Dengue predicted Cases vs. Actual Cases (City-San Juan) ')

# Evaluate RMSE and MAE on the validation data
RMSE.SJ.rf <- sqrt(mean((prediction_sj.rf-validation.sj$total_cases)^2))
RMSE.SJ.rf
## [1] 34.51898
MAE.SJ.rf <- mean(abs(prediction_sj.rf-validation.sj$total_cases))
MAE.SJ.rf
## [1] 24.67019
rm(df_prediction_sj.rf, model_sj.rf, prediction_sj.rf)

RF without CV for IQ

library(reshape2)
library(ggplot2)
library(randomForest)
library(caret)

set.seed(136)

#Build the model
model_iq.rf <- randomForest(formula = total_cases ~ ., data = train.iq[,4:24])

model_iq.rf
## 
## Call:
##  randomForest(formula = total_cases ~ ., data = train.iq[, 4:24]) 
##                Type of random forest: regression
##                      Number of trees: 500
## No. of variables tried at each split: 6
## 
##           Mean of squared residuals: 106.1351
##                     % Var explained: 3.88
prediction_iq.rf <-  predict(model_iq.rf, validation.iq, type = 'response')

#Plot the prediction for NBR
df_prediction_iq.rf <- data.frame('prediction' = prediction_iq.rf,
                                   'actual' = validation.iq$total_cases,
                                   'time' = validation.iq$week_start_date)

df_prediction_iq.rf <- melt(df_prediction_iq.rf, id.vars = 'time')

ggplot(df_prediction_iq.rf, aes(x = time, y = value, color = variable)) +
  geom_line() +
  ggtitle('RF: Dengue predicted Cases vs. Actual Cases (City-IQUITOS) ')

# Evaluate RMSE and MAE on the validation data
RMSE.IQ.rf <- sqrt(mean((prediction_iq.rf-validation.iq$total_cases)^2))
RMSE.IQ.rf
## [1] 11.67287
MAE.IQ.rf <- mean(abs(prediction_iq.rf-validation.iq$total_cases))
MAE.IQ.rf
## [1] 7.544626
rm(df_prediction_iq.rf, model_iq.rf, prediction_iq.rf)

Multi Layer Perceptron

MLP for SJ

MLP for IQ

EVALUATION OF PREDICTIVE MODELS

MAE and RMSE for Predictive Models

MAE and RMSE for SJ

# Create a data frame with the error metrics for each method
accuracy <- data.frame(Method = c("Baseline1",
                                  "Baseline2",
                                  "NB Regression",
                                  "SVM",
                                  "Random forest"),
                       RMSE   = c(RMSE.SJ.baseline1,
                                  RMSE.SJ.baseline2,
                                  RMSE.SJ.svm,
                                  RMSE.SJ.nbr,
                                  RMSE.SJ.rf),
                       MAE    = c(MAE.SJ.baseline1,
                                  MAE.SJ.baseline2,
                                  MAE.SJ.svm,
                                  MAE.SJ.nbr,
                                  MAE.SJ.rf)) 
 
# Round the values and print the table
accuracy$RMSE <- round(accuracy$RMSE,2)
accuracy$MAE <- round(accuracy$MAE,2) 
 
accuracy
##          Method  RMSE   MAE
## 1     Baseline1 15.34  8.39
## 2     Baseline2 44.42 27.03
## 3 NB Regression 42.23 19.77
## 4           SVM 42.50 26.16
## 5 Random forest 34.52 24.67
rm(accuracy, 
   RMSE.SJ.baseline1,
   RMSE.SJ.baseline2,
   RMSE.SJ.svm,
   RMSE.SJ.nbr,
   RMSE.SJ.rf,
   MAE.SJ.baseline1,
   MAE.SJ.baseline2,
   MAE.SJ.svm,
   MAE.SJ.nbr,
   MAE.SJ.rf)

MAE and RMSE for IQ

# Create a data frame with the error metrics for each method
accuracy <- data.frame(Method = c("Baseline1",
                                  "Baseline2",
                                  "NB Regression",
                                  "SVM",
                                  "Random forest"),
                       RMSE   = c(RMSE.IQ.baseline1,
                                  RMSE.IQ.baseline2,
                                  RMSE.IQ.svm,
                                  RMSE.IQ.nbr,
                                  RMSE.IQ.rf),
                       MAE    = c(MAE.IQ.baseline1,
                                  MAE.IQ.baseline2,
                                  MAE.IQ.svm,
                                  MAE.IQ.nbr,
                                  MAE.IQ.rf)) 
 
# Round the values and print the table
accuracy$RMSE <- round(accuracy$RMSE,2)
accuracy$MAE <- round(accuracy$MAE,2) 
 
accuracy
##          Method  RMSE  MAE
## 1     Baseline1  7.66 3.93
## 2     Baseline2 11.70 7.38
## 3 NB Regression 11.95 6.40
## 4           SVM 11.93 7.74
## 5 Random forest 11.67 7.54
rm(accuracy, 
   RMSE.IQ.baseline1,
   RMSE.IQ.baseline2,
   RMSE.IQ.svm,
   RMSE.IQ.nbr,
   RMSE.IQ.rf,
   MAE.IQ.baseline1,
   MAE.IQ.baseline2,
   MAE.IQ.svm,
   MAE.IQ.nbr,
   MAE.IQ.rf)

Drop training and validation sets

rm(train.iq, train.sj, validation.iq, validation.sj)

CROSS-VALIDATION - Build training and validation sets

CV for SJ

library(caret)

set.seed(136)

methods <- c("rf", "mlp", "rpart", "svmLinear", "svmRadial",  "parRF", "avNNet", "xgbTree", "xgbLinear")
performetrics <- data.frame()
#trainControl
control <- trainControl(method="repeatedcv", number=10, repeats=3)

for (i in 1:length(methods)){
  #Train the model
  print(methods[i])
  model_sj.cv <- train(total_cases~.,
                       data=sj_train_labels.lastna[3:23],
                       method=methods[i],
                       trControl=control)
  # summarize results
  #print(methods[i])
  #model_sj.cv$results["MAE"]
  #model_sj.cv$results["RMSE"]
  performetrics[i,1] <- methods[i]
  performetrics[i,2] <- min(model_sj.cv$results["MAE"])
  performetrics[i,3] <- min(model_sj.cv$results["RMSE"])  

}
## [1] "rf"
## [1] "mlp"
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## [1] "rpart"
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## [1] "svmLinear"
## [1] "svmRadial"
## [1] "parRF"
## Warning: executing %dopar% sequentially: no parallel backend registered
## [1] "avNNet"
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3033412.551635 
## final  value 3002363.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3023730.533827 
## final  value 3002363.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3043041.143716 
## final  value 3002363.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3044915.064948 
## final  value 3002363.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3038122.658842 
## final  value 3002363.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3032663.486731 
## final  value 3002363.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3027053.278911 
## final  value 3002363.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3019352.498105 
## final  value 3002363.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3034078.744770 
## final  value 3002363.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3021944.201776 
## final  value 3002363.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3021995.344081 
## final  value 3002363.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3042900.747604 
## final  value 3002363.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3024983.659338 
## final  value 3002363.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3016558.934887 
## final  value 3002363.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3010524.747937 
## final  value 3002363.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3039981.628924 
## iter  10 value 3002375.583968
## final  value 3002369.966597 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3036432.634767 
## iter  10 value 3002371.612992
## final  value 3002370.002670 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3021129.104367 
## iter  10 value 3002397.785815
## iter  20 value 3002370.097068
## final  value 3002369.961312 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3030668.543139 
## iter  10 value 3002383.774959
## final  value 3002369.957054 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3027459.602369 
## iter  10 value 3002377.548934
## final  value 3002370.018234 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3040955.687735 
## iter  10 value 3002382.036176
## final  value 3002366.927385 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3038499.647328 
## iter  10 value 3002374.077701
## final  value 3002370.222007 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3030154.795268 
## iter  10 value 3002617.526354
## iter  20 value 3002367.565298
## final  value 3002366.890190 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3033374.214222 
## iter  10 value 3002550.931790
## iter  20 value 3002367.104903
## iter  20 value 3002367.078674
## final  value 3002366.869517 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3028995.293692 
## iter  10 value 3002370.984877
## iter  20 value 3002368.313058
## final  value 3002366.901943 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3049772.655795 
## iter  10 value 3002372.399733
## iter  20 value 3002366.038706
## final  value 3002365.848255 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3048342.158213 
## iter  10 value 3002581.439844
## iter  20 value 3002367.053344
## final  value 3002366.294348 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3027971.356925 
## iter  10 value 3002528.963883
## iter  20 value 3002366.001046
## final  value 3002365.789109 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3043359.207005 
## iter  10 value 3002529.717082
## iter  20 value 3002368.440837
## iter  30 value 3002365.815339
## final  value 3002365.763168 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3015946.473671 
## iter  10 value 3002527.270675
## final  value 3002365.824885 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3027030.955665 
## iter  10 value 3002429.817444
## iter  20 value 3002363.775378
## final  value 3002363.057977 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3032610.790682 
## iter  10 value 3002397.082072
## iter  20 value 3002363.399202
## final  value 3002363.048484 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3026830.170114 
## iter  10 value 3002428.306540
## iter  20 value 3002363.758032
## final  value 3002363.056855 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3029366.111399 
## iter  10 value 3002397.269704
## iter  20 value 3002363.400886
## final  value 3002363.048232 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3030316.846580 
## iter  10 value 3002431.685720
## iter  20 value 3002363.797427
## final  value 3002363.059109 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3034105.408377 
## iter  10 value 3002418.290929
## iter  20 value 3002363.657294
## final  value 3002363.063777 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3019631.090411 
## iter  10 value 3002421.757244
## iter  20 value 3002363.694800
## final  value 3002363.064044 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3034789.582354 
## final  value 3002371.612698 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3032559.481795 
## iter  10 value 3002446.824295
## iter  20 value 3002363.985177
## final  value 3002363.084228 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3015960.297890 
## iter  10 value 3002416.130587
## iter  20 value 3002363.628690
## final  value 3002363.076856 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3013772.495394 
## final  value 3002367.883099 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3013946.881898 
## final  value 3002363.610919 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3040629.102581 
## iter  10 value 3002436.604744
## iter  20 value 3002363.878975
## final  value 3002363.114553 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3027612.136445 
## final  value 3002365.019129 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3036564.616250 
## iter  10 value 3002494.706970
## iter  20 value 3002364.544476
## final  value 3002363.159501 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3315679.916131 
## final  value 3296748.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3326563.804931 
## final  value 3296748.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3320797.093314 
## final  value 3296748.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3321435.732421 
## final  value 3296748.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3319355.415410 
## final  value 3296748.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3317406.432069 
## final  value 3296748.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3314353.712419 
## final  value 3296748.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3324995.318952 
## final  value 3296748.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3315428.274232 
## final  value 3296748.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3315831.986299 
## final  value 3296748.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3324094.185795 
## final  value 3296748.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3321756.427280 
## final  value 3296748.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3317839.631507 
## final  value 3296748.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3310696.343662 
## final  value 3296748.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3331938.300769 
## final  value 3296748.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3339493.753851 
## iter  10 value 3297441.003730
## iter  20 value 3296759.457748
## final  value 3296754.982560 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3320244.506167 
## iter  10 value 3296755.231227
## final  value 3296754.984220 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3314492.709403 
## iter  10 value 3296760.798396
## final  value 3296754.984362 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3312002.455247 
## iter  10 value 3296757.058508
## final  value 3296755.026511 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3327034.139423 
## iter  10 value 3296872.722698
## iter  20 value 3296755.005353
## iter  20 value 3296754.988585
## iter  20 value 3296754.988142
## final  value 3296754.988142 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3317184.180001 
## iter  10 value 3296889.748083
## iter  20 value 3296753.040253
## final  value 3296751.884253 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3324420.004218 
## iter  10 value 3296986.844356
## iter  20 value 3296752.186842
## final  value 3296751.884147 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3331178.644922 
## iter  10 value 3297179.115947
## iter  20 value 3296754.465385
## final  value 3296751.927436 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3327051.586480 
## iter  10 value 3296758.164576
## final  value 3296751.883622 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3323064.230563 
## iter  10 value 3296972.904370
## final  value 3296751.944310 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3329614.281090 
## iter  10 value 3296800.097812
## iter  20 value 3296753.808534
## iter  30 value 3296751.464875
## final  value 3296751.035778 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3337349.186516 
## iter  10 value 3297310.320248
## iter  20 value 3296756.938260
## final  value 3296751.260719 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3325955.605330 
## iter  10 value 3296755.213332
## iter  20 value 3296750.922244
## final  value 3296750.777460 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3325446.155905 
## iter  10 value 3296926.487723
## iter  20 value 3296751.296611
## final  value 3296750.762804 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3314183.389328 
## iter  10 value 3296976.814307
## iter  20 value 3296751.929813
## final  value 3296751.396190 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3334161.392392 
## iter  10 value 3296916.414645
## iter  20 value 3296749.946031
## final  value 3296748.127297 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3317928.077905 
## iter  10 value 3296781.261195
## iter  20 value 3296748.390026
## final  value 3296748.047757 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3329840.602336 
## iter  10 value 3296803.628136
## iter  20 value 3296748.648258
## final  value 3296748.051017 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3331739.567602 
## iter  10 value 3296809.415712
## iter  20 value 3296748.714299
## final  value 3296748.054909 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3321653.483431 
## iter  10 value 3296815.822218
## iter  20 value 3296748.785535
## final  value 3296748.057334 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3319780.582936 
## iter  10 value 3296814.730163
## iter  20 value 3296748.784681
## final  value 3296748.068301 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3328948.925143 
## iter  10 value 3296795.911818
## iter  20 value 3296748.570688
## final  value 3296748.074915 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3326779.241489 
## final  value 3296748.057495 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3327879.569964 
## iter  10 value 3296900.354496
## iter  20 value 3296749.773751
## final  value 3296748.109789 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3326451.402514 
## iter  10 value 3296859.375550
## iter  20 value 3296749.303297
## final  value 3296748.146393 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3319573.890671 
## iter  10 value 3296891.278602
## iter  20 value 3296749.680848
## final  value 3296748.075655 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3328388.333042 
## iter  10 value 3296828.060186
## iter  20 value 3296748.955098
## final  value 3296748.072897 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3322913.232549 
## iter  10 value 3296913.030251
## iter  20 value 3296749.931618
## final  value 3296748.094845 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3332062.636779 
## iter  10 value 3296963.488751
## iter  20 value 3296750.513144
## final  value 3296748.129613 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3332018.327859 
## iter  10 value 3296897.402253
## iter  20 value 3296749.750948
## final  value 3296748.107355 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3274924.977965 
## final  value 3248505.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3264378.181139 
## final  value 3248505.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3276924.374271 
## final  value 3248505.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3284304.615713 
## final  value 3248505.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3286220.176548 
## final  value 3248505.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3277713.272891 
## final  value 3248505.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3289355.357855 
## final  value 3248505.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3290858.139184 
## final  value 3248505.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3267055.206375 
## final  value 3248505.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3274770.028179 
## final  value 3248505.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3278359.821359 
## final  value 3248505.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3276068.670165 
## final  value 3248505.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3281690.943355 
## final  value 3248505.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3288613.811388 
## final  value 3248505.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3287754.973431 
## final  value 3248505.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3287773.187293 
## iter  10 value 3248532.975836
## final  value 3248511.993714 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3284048.956745 
## iter  10 value 3248650.243604
## iter  20 value 3248517.547631
## iter  20 value 3248517.533920
## iter  20 value 3248517.524311
## final  value 3248517.524311 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3277570.049161 
## iter  10 value 3248608.192692
## iter  20 value 3248515.173485
## final  value 3248511.982271 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3270011.055095 
## iter  10 value 3248597.293483
## final  value 3248512.010280 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3273920.889224 
## iter  10 value 3248519.922620
## final  value 3248517.503955 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3278773.289101 
## iter  10 value 3248705.005184
## iter  20 value 3248525.232655
## iter  30 value 3248508.906770
## iter  30 value 3248508.903799
## iter  30 value 3248508.902631
## final  value 3248508.902631 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3271105.503240 
## iter  10 value 3248517.244421
## final  value 3248508.883007 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3294105.620462 
## iter  10 value 3248547.791930
## iter  20 value 3248512.804985
## iter  30 value 3248510.063016
## final  value 3248508.880666 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3288969.094079 
## iter  10 value 3248554.626690
## iter  20 value 3248513.067518
## final  value 3248508.918401 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3288383.289930 
## iter  10 value 3248524.904530
## iter  20 value 3248508.947350
## final  value 3248508.883948 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3278868.579739 
## iter  10 value 3248853.194905
## iter  20 value 3248509.262881
## final  value 3248507.776614 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3294197.016695 
## iter  10 value 3248564.247239
## iter  20 value 3248527.999408
## iter  30 value 3248508.209943
## final  value 3248507.751473 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3270932.014412 
## iter  10 value 3248514.372587
## iter  20 value 3248507.910954
## final  value 3248507.787847 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3279076.154733 
## iter  10 value 3248597.543653
## iter  20 value 3248509.217961
## final  value 3248507.782267 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3276980.727552 
## iter  10 value 3248873.536507
## iter  20 value 3248509.255796
## final  value 3248507.815777 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3260727.967994 
## iter  10 value 3248537.393227
## iter  20 value 3248505.377267
## final  value 3248505.061550 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3276128.737098 
## iter  10 value 3248576.462726
## iter  20 value 3248505.832033
## final  value 3248505.064779 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3284358.599042 
## iter  10 value 3248588.778242
## iter  20 value 3248505.973171
## final  value 3248505.068478 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3277212.385759 
## iter  10 value 3248540.840232
## iter  20 value 3248505.418167
## final  value 3248505.049343 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3280292.668499 
## iter  10 value 3248578.736209
## iter  20 value 3248505.855840
## final  value 3248505.043135 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3282028.301726 
## final  value 3248556.226885 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3289951.064941 
## iter  10 value 3248588.596734
## iter  20 value 3248505.982527
## final  value 3248505.114207 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3285421.170357 
## iter  10 value 3248652.380271
## iter  20 value 3248506.714747
## final  value 3248505.063493 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3280239.064791 
## iter  10 value 3248593.885741
## iter  20 value 3248506.040516
## final  value 3248505.080734 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3263035.989289 
## iter  10 value 3248566.914374
## iter  20 value 3248505.730994
## final  value 3248505.087916 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3277316.391579 
## iter  10 value 3248648.200456
## iter  20 value 3248506.681364
## final  value 3248505.135136 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3288140.992708 
## iter  10 value 3248560.945238
## iter  20 value 3248505.672327
## final  value 3248505.127223 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3274744.153508 
## iter  10 value 3248608.981532
## iter  20 value 3248506.225669
## final  value 3248505.102952 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3266189.007755 
## final  value 3248505.054683 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3277058.249532 
## iter  10 value 3248649.973154
## iter  20 value 3248506.698278
## final  value 3248505.074083 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 2846894.969331 
## final  value 2820375.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 2844739.916312 
## final  value 2820375.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 2849697.524969 
## final  value 2820375.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 2857778.977515 
## final  value 2820375.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 2839553.376385 
## final  value 2820375.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 2851139.803079 
## final  value 2820375.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 2855049.841185 
## final  value 2820375.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 2848060.644109 
## final  value 2820375.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 2848163.608626 
## final  value 2820375.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 2838411.738688 
## final  value 2820375.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 2838068.541547 
## final  value 2820375.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 2843610.404142 
## final  value 2820375.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 2846093.710297 
## final  value 2820375.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 2848619.021916 
## final  value 2820375.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 2843260.988574 
## final  value 2820375.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 2849681.763721 
## iter  10 value 2820382.386022
## final  value 2820381.942578 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 2834022.626571 
## iter  10 value 2820396.962266
## final  value 2820381.952470 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 2845643.792953 
## iter  10 value 2820481.994478
## final  value 2820381.945855 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 2846059.561438 
## iter  10 value 2820466.506986
## final  value 2820381.941046 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 2849404.556145 
## iter  10 value 2820391.362083
## final  value 2820381.985515 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 2847064.200350 
## iter  10 value 2820708.694007
## iter  20 value 2820421.158169
## iter  30 value 2820379.719080
## final  value 2820378.888078 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 2859278.882130 
## iter  10 value 2820382.721235
## final  value 2820378.865843 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 2856304.652943 
## iter  10 value 2820385.100097
## final  value 2820378.939538 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 2861209.675985 
## iter  10 value 2820390.378001
## iter  20 value 2820380.040004
## final  value 2820379.955452 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 2840171.060607 
## iter  10 value 2820491.522615
## iter  20 value 2820378.966224
## iter  20 value 2820378.940818
## iter  20 value 2820378.940422
## final  value 2820378.940422 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 2846322.464230 
## iter  10 value 2820436.999191
## iter  20 value 2820383.655231
## final  value 2820379.486293 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 2867624.566165 
## iter  10 value 2820384.150550
## iter  20 value 2820377.808277
## final  value 2820377.747288 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 2847842.980219 
## iter  10 value 2820402.042034
## iter  20 value 2820379.510215
## iter  30 value 2820377.949137
## final  value 2820377.733672 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 2832085.364202 
## iter  10 value 2820583.310278
## iter  20 value 2820379.507483
## final  value 2820377.785003 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 2843341.219587 
## iter  10 value 2820425.475614
## iter  20 value 2820377.914754
## final  value 2820377.759899 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 2849475.101921 
## iter  10 value 2820408.298952
## iter  20 value 2820375.391319
## final  value 2820375.048669 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 2846855.781572 
## iter  10 value 2820408.374201
## iter  20 value 2820375.391395
## final  value 2820375.047964 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 2850038.135375 
## iter  10 value 2820441.086469
## iter  20 value 2820375.767577
## final  value 2820375.053932 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 2844517.987776 
## iter  10 value 2820454.619723
## iter  20 value 2820375.923648
## final  value 2820375.046092 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 2841088.213383 
## iter  10 value 2820433.757210
## iter  20 value 2820375.683485
## final  value 2820375.073116 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 2854623.015241 
## iter  10 value 2820466.499570
## iter  20 value 2820376.071605
## final  value 2820375.063192 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 2838374.342135 
## iter  10 value 2820442.197005
## iter  20 value 2820375.788044
## final  value 2820375.050887 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 2860016.283973 
## iter  10 value 2820423.948238
## iter  20 value 2820375.580722
## final  value 2820375.076124 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 2841492.150086 
## iter  10 value 2820448.710070
## iter  20 value 2820375.868500
## final  value 2820375.077075 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 2854251.923191 
## iter  10 value 2820421.782973
## iter  20 value 2820375.557063
## final  value 2820375.054881 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 2831589.817357 
## iter  10 value 2820441.003332
## iter  20 value 2820375.789932
## final  value 2820375.156216 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 2841106.421775 
## iter  10 value 2820525.666317
## iter  20 value 2820376.766106
## final  value 2820375.099649 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 2843728.661691 
## iter  10 value 2820481.857871
## iter  20 value 2820376.254857
## final  value 2820375.101046 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 2858744.406538 
## iter  10 value 2820556.221500
## iter  20 value 2820377.117006
## final  value 2820375.112534 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 2855304.957762 
## iter  10 value 2820428.918432
## iter  20 value 2820375.647466
## final  value 2820375.087525 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3260531.422304 
## final  value 3227945.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3255061.698676 
## final  value 3227945.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3252399.122668 
## final  value 3227945.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3252833.308422 
## final  value 3227945.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3252094.336993 
## final  value 3227945.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3247094.554790 
## final  value 3227945.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3257005.849573 
## final  value 3227945.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3245083.452090 
## final  value 3227945.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3261330.566655 
## final  value 3227945.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3256252.049642 
## final  value 3227945.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3256389.446689 
## final  value 3227945.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3259401.277441 
## final  value 3227945.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3253836.688428 
## final  value 3227945.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3263924.308953 
## final  value 3227945.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3257273.241690 
## final  value 3227945.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3247931.422227 
## iter  10 value 3227988.809106
## iter  20 value 3227952.779449
## final  value 3227951.981590 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3256324.046275 
## iter  10 value 3227965.339616
## iter  20 value 3227952.041456
## iter  20 value 3227952.010562
## iter  20 value 3227952.006956
## final  value 3227952.006956 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3270838.535116 
## iter  10 value 3235161.723129
## iter  20 value 3227960.230102
## final  value 3227951.978860 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3250715.769897 
## iter  10 value 3228041.548542
## iter  20 value 3227953.752677
## final  value 3227952.007645 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3261763.737703 
## iter  10 value 3227957.610680
## final  value 3227952.043980 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3257623.728749 
## iter  10 value 3228055.257543
## iter  20 value 3227949.361092
## final  value 3227948.901513 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3242982.387950 
## iter  10 value 3227983.419169
## iter  20 value 3227950.302838
## final  value 3227950.148190 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3236769.014977 
## iter  10 value 3227953.876906
## iter  20 value 3227949.628168
## final  value 3227948.919053 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3255962.294323 
## iter  10 value 3227955.587492
## final  value 3227949.435919 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3259499.486198 
## iter  10 value 3227960.944469
## iter  20 value 3227950.141942
## iter  20 value 3227950.121575
## iter  30 value 3227948.906440
## iter  30 value 3227948.895271
## iter  30 value 3227948.895271
## final  value 3227948.895271 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3262298.798015 
## iter  10 value 3227971.704234
## iter  20 value 3227948.391735
## final  value 3227947.788758 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3268187.556761 
## iter  10 value 3227955.492536
## iter  20 value 3227947.996147
## final  value 3227947.768297 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3261831.125417 
## iter  10 value 3228160.301590
## iter  20 value 3227949.123955
## final  value 3227948.275324 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3253572.375850 
## iter  10 value 3228353.397491
## iter  20 value 3227947.989883
## final  value 3227947.803028 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3255789.181543 
## iter  10 value 3228058.978969
## iter  20 value 3227952.557227
## iter  30 value 3227948.765326
## final  value 3227947.769386 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3248747.676726 
## iter  10 value 3227990.898661
## iter  20 value 3227945.533634
## final  value 3227945.061286 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3253268.501323 
## iter  10 value 3228000.332018
## iter  20 value 3227945.643443
## final  value 3227945.049370 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3254757.831070 
## iter  10 value 3228001.491048
## iter  20 value 3227945.655865
## final  value 3227945.049339 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3250701.887250 
## iter  10 value 3227978.053490
## iter  20 value 3227945.386374
## final  value 3227945.046233 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3260063.297750 
## iter  10 value 3227986.569949
## iter  20 value 3227945.483716
## final  value 3227945.055918 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3258134.233678 
## iter  10 value 3228092.263262
## iter  20 value 3227946.714333
## final  value 3227945.064397 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3263022.419272 
## iter  10 value 3227979.984651
## iter  20 value 3227945.419602
## final  value 3227945.059674 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3243506.704449 
## iter  10 value 3227990.102201
## iter  20 value 3227945.537167
## final  value 3227945.073118 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3263324.037679 
## iter  10 value 3228041.897604
## iter  20 value 3227946.133640
## final  value 3227945.087341 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3256068.473308 
## iter  10 value 3228016.567257
## iter  20 value 3227945.843796
## final  value 3227945.075505 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3268301.839367 
## iter  10 value 3228067.292851
## iter  20 value 3227946.438463
## final  value 3227945.085873 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3262476.022597 
## iter  10 value 3228075.734594
## iter  20 value 3227946.536054
## final  value 3227945.071415 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3261143.098963 
## iter  10 value 3228071.165829
## iter  20 value 3227946.482847
## final  value 3227945.172362 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3267648.390182 
## iter  10 value 3228073.319252
## iter  20 value 3227946.505308
## final  value 3227945.123746 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3266557.399527 
## final  value 3227945.097044 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3196452.588220 
## final  value 3173738.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3194759.839199 
## final  value 3173738.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3205303.562509 
## final  value 3173738.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3201477.788596 
## final  value 3173738.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3215887.084748 
## final  value 3173738.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3204681.284998 
## final  value 3173738.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3213782.285526 
## final  value 3173738.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3207654.476755 
## final  value 3173738.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3193250.639233 
## final  value 3173738.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3203561.708766 
## final  value 3173738.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3191591.284737 
## final  value 3173738.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3203800.127412 
## final  value 3173738.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3202450.517017 
## final  value 3173738.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3205021.842863 
## final  value 3173738.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3203885.970604 
## final  value 3173738.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3193562.909693 
## iter  10 value 3173782.586750
## iter  20 value 3173745.409883
## final  value 3173745.002371 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3198929.271708 
## iter  10 value 3173879.710963
## final  value 3173744.984759 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3215193.025985 
## iter  10 value 3173750.692148
## final  value 3173744.979737 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3198027.774277 
## iter  10 value 3173930.325272
## iter  20 value 3173745.153777
## iter  20 value 3173745.125026
## final  value 3173744.987804 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3217448.125112 
## iter  10 value 3174361.886392
## iter  20 value 3173753.469159
## final  value 3173750.523235 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3204978.810262 
## iter  10 value 3173795.815029
## iter  20 value 3173742.298738
## final  value 3173741.908538 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3198638.732787 
## iter  10 value 3173745.895904
## final  value 3173741.904386 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3196701.154525 
## iter  10 value 3173747.399601
## final  value 3173741.898121 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3203221.993784 
## iter  10 value 3173934.222598
## iter  20 value 3173742.630396
## final  value 3173741.925044 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3204387.605831 
## iter  10 value 3173900.970351
## iter  20 value 3173747.081456
## final  value 3173742.413068 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3219928.777265 
## iter  10 value 3173820.040818
## iter  20 value 3173742.614500
## final  value 3173740.995593 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3196279.948157 
## iter  10 value 3174159.728431
## iter  20 value 3173741.252437
## iter  20 value 3173741.228865
## final  value 3173740.770046 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3199040.845493 
## iter  10 value 3173862.537160
## iter  20 value 3173745.965268
## iter  30 value 3173740.958648
## final  value 3173740.766800 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3205165.999546 
## iter  10 value 3173750.479127
## iter  20 value 3173741.305472
## final  value 3173741.023411 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3193707.517358 
## iter  10 value 3173799.743863
## iter  20 value 3173742.473859
## final  value 3173740.750293 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3210934.683771 
## iter  10 value 3174473.080512
## iter  20 value 3173746.474901
## iter  30 value 3173738.097709
## final  value 3173738.040002 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3195245.742775 
## iter  10 value 3173794.716347
## iter  20 value 3173738.659940
## final  value 3173738.051007 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3200902.841667 
## iter  10 value 3173773.593348
## iter  20 value 3173738.416942
## final  value 3173738.050672 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3197511.573799 
## iter  10 value 3173805.263570
## iter  20 value 3173738.780454
## final  value 3173738.058263 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3215620.515652 
## iter  10 value 3173806.472961
## iter  20 value 3173738.796504
## final  value 3173738.085208 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3199259.746491 
## iter  10 value 3173841.877111
## iter  20 value 3173739.217064
## final  value 3173738.072243 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3195709.893339 
## iter  10 value 3173780.801381
## iter  20 value 3173738.508531
## final  value 3173738.068145 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3215134.874215 
## iter  10 value 3173803.640029
## iter  20 value 3173738.775756
## final  value 3173738.136089 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3195841.282012 
## iter  10 value 3173802.389636
## iter  20 value 3173738.759873
## final  value 3173738.091082 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3204164.223575 
## iter  10 value 3173838.496763
## iter  20 value 3173739.178516
## final  value 3173738.134635 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3201336.990290 
## final  value 3173752.129763 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3191011.524712 
## final  value 3173738.048296 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3187815.320351 
## final  value 3173738.045348 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3186423.464070 
## iter  10 value 3173807.476195
## iter  20 value 3173738.829166
## final  value 3173738.083404 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3216353.919766 
## iter  10 value 3173779.370453
## iter  20 value 3173738.505056
## final  value 3173738.102023 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3213927.531452 
## final  value 3194851.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3223208.925582 
## final  value 3194851.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3219280.577002 
## final  value 3194851.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3230086.302541 
## final  value 3194851.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3224255.087311 
## final  value 3194851.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3230698.776565 
## final  value 3194851.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3221769.956384 
## final  value 3194851.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3215082.841325 
## final  value 3194851.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3235837.841605 
## final  value 3194851.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3219754.869460 
## final  value 3194851.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3228365.848615 
## final  value 3194851.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3235821.177177 
## final  value 3194851.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3230882.964109 
## final  value 3194851.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3220634.699260 
## final  value 3194851.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3224491.855954 
## final  value 3194851.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3220511.876243 
## iter  10 value 3194860.767442
## final  value 3194857.983954 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3222828.616926 
## iter  10 value 3194915.949504
## final  value 3194858.026257 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3221957.693754 
## iter  10 value 3194865.767022
## final  value 3194858.010932 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3214670.717917 
## iter  10 value 3194937.398849
## final  value 3194857.985901 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3221120.201729 
## iter  10 value 3195869.110765
## iter  20 value 3194862.987986
## final  value 3194858.025158 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3231303.698498 
## iter  10 value 3194955.253009
## final  value 3194854.917519 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3221906.125298 
## iter  10 value 3195000.975963
## iter  20 value 3194861.378867
## iter  30 value 3194855.900982
## final  value 3194854.936345 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3224411.382711 
## iter  10 value 3194859.961362
## iter  20 value 3194855.019129
## iter  20 value 3194854.997055
## final  value 3194854.922961 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3219847.280532 
## iter  10 value 3195059.539189
## iter  20 value 3194855.251231
## final  value 3194854.906412 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3214926.581666 
## iter  10 value 3195313.891944
## iter  20 value 3194855.834568
## final  value 3194854.880771 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3230994.556169 
## iter  10 value 3194866.880005
## iter  20 value 3194854.457009
## final  value 3194853.771711 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3218204.623334 
## iter  10 value 3195245.291252
## iter  20 value 3194854.648285
## final  value 3194853.774410 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3231768.740217 
## iter  10 value 3194909.622394
## iter  20 value 3194854.473287
## final  value 3194853.788184 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3214257.887988 
## iter  10 value 3195034.114722
## iter  20 value 3194858.143017
## iter  30 value 3194855.649556
## iter  40 value 3194854.042940
## final  value 3194853.827173 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3232063.093812 
## iter  10 value 3195246.449779
## final  value 3194854.922436 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3214986.618703 
## iter  10 value 3194881.016558
## iter  20 value 3194851.353171
## final  value 3194851.044301 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3208155.888765 
## iter  10 value 3194902.491616
## iter  20 value 3194851.599761
## final  value 3194851.046928 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3231203.764960 
## iter  10 value 3194886.233222
## iter  20 value 3194851.410781
## final  value 3194851.048201 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3227717.856988 
## iter  10 value 3194892.465152
## iter  20 value 3194851.484938
## final  value 3194851.058239 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3218109.878263 
## iter  10 value 3194918.895986
## iter  20 value 3194851.788092
## final  value 3194851.059114 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3212840.457461 
## iter  10 value 3194939.402202
## iter  20 value 3194852.033009
## final  value 3194851.089950 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3235014.425246 
## iter  10 value 3194949.862469
## iter  20 value 3194852.156172
## final  value 3194851.066600 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3223924.284875 
## iter  10 value 3194983.175233
## iter  20 value 3194852.539955
## final  value 3194851.059075 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3226746.154333 
## iter  10 value 3194896.351917
## iter  20 value 3194851.538433
## final  value 3194851.071801 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3214858.111436 
## iter  10 value 3194944.518567
## iter  20 value 3194852.096380
## final  value 3194851.085031 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3225079.005658 
## iter  10 value 3194985.369817
## iter  20 value 3194852.577055
## final  value 3194851.126736 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3229243.487412 
## iter  10 value 3195055.582816
## iter  20 value 3194853.386111
## final  value 3194851.276777 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3227010.108904 
## final  value 3194854.823631 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3215745.343898 
## iter  10 value 3194925.159769
## iter  20 value 3194851.886627
## final  value 3194851.069459 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3230207.256015 
## final  value 3194851.042311 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3129392.022452 
## final  value 3094700.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3113915.166334 
## final  value 3094700.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3119496.769283 
## final  value 3094700.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3124676.384634 
## final  value 3094700.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3116869.245361 
## final  value 3094700.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3124037.937106 
## final  value 3094700.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3133083.961197 
## final  value 3094700.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3131778.571718 
## final  value 3094700.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3119200.641166 
## final  value 3094700.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3114117.588295 
## final  value 3094700.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3118247.457840 
## final  value 3094700.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3107271.475657 
## final  value 3094700.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3125391.875998 
## final  value 3094700.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3125254.025212 
## final  value 3094700.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3141716.270653 
## final  value 3094700.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3115228.923719 
## iter  10 value 3094902.039171
## final  value 3094706.972653 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3120066.367981 
## iter  10 value 3094722.433357
## final  value 3094712.476483 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3119790.652306 
## iter  10 value 3094712.239752
## final  value 3094706.978615 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3130187.765093 
## iter  10 value 3094813.580759
## iter  20 value 3094707.602454
## final  value 3094706.994091 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3131233.807650 
## iter  10 value 3094716.214477
## final  value 3094712.515976 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3133722.002150 
## iter  10 value 3094771.168014
## iter  20 value 3094706.588939
## final  value 3094704.975293 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3122999.359944 
## iter  10 value 3094725.473698
## final  value 3094703.906350 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3120360.218573 
## iter  10 value 3094710.749862
## iter  20 value 3094703.937561
## final  value 3094703.897963 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3115221.056369 
## iter  10 value 3094707.191600
## iter  20 value 3094703.880276
## iter  20 value 3094703.875768
## iter  20 value 3094703.875262
## final  value 3094703.875262 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3132185.180625 
## iter  10 value 3094899.963731
## iter  20 value 3094705.537376
## iter  30 value 3094703.893560
## iter  30 value 3094703.880809
## iter  30 value 3094703.875188
## final  value 3094703.875188 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3119562.968128 
## iter  10 value 3094717.584749
## final  value 3094703.960929 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3111333.436829 
## iter  10 value 3094710.557617
## iter  20 value 3094702.877829
## iter  20 value 3094702.860020
## final  value 3094702.771411 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3129454.361175 
## iter  10 value 3094719.634265
## iter  20 value 3094703.463224
## final  value 3094702.750968 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3120778.644616 
## iter  10 value 3094998.503069
## iter  20 value 3094708.455277
## iter  30 value 3094702.996574
## final  value 3094702.774199 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3121372.687354 
## iter  10 value 3094895.305910
## iter  20 value 3094704.142977
## final  value 3094702.833720 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3123489.625626 
## iter  10 value 3094735.090569
## iter  20 value 3094700.410365
## final  value 3094700.049263 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3124175.337928 
## iter  10 value 3094740.769340
## iter  20 value 3094700.475785
## final  value 3094700.056238 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3115760.644527 
## iter  10 value 3094730.767219
## iter  20 value 3094700.360264
## final  value 3094700.043656 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3119553.208825 
## iter  10 value 3094768.212783
## iter  20 value 3094700.791553
## final  value 3094700.059171 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3134620.143258 
## iter  10 value 3094756.906972
## iter  20 value 3094700.662481
## final  value 3094700.071337 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3132837.233110 
## iter  10 value 3094753.204183
## iter  20 value 3094700.627708
## final  value 3094700.056592 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3122519.624421 
## iter  10 value 3094786.279918
## iter  20 value 3094701.011760
## final  value 3094700.060883 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3121643.727013 
## iter  10 value 3094771.118411
## iter  20 value 3094700.839129
## final  value 3094700.071251 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3126326.113618 
## iter  10 value 3094787.780223
## iter  20 value 3094701.030697
## final  value 3094700.088128 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3132279.104604 
## iter  10 value 3094784.021061
## iter  20 value 3094700.985018
## final  value 3094700.081934 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3123119.611533 
## iter  10 value 3094838.837035
## iter  20 value 3094701.627470
## final  value 3094700.168771 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3116092.940952 
## iter  10 value 3094839.592900
## iter  20 value 3094701.642330
## final  value 3094700.078466 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3127545.418190 
## final  value 3094700.916718 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3118136.572781 
## iter  10 value 3094767.928399
## iter  20 value 3094700.814240
## final  value 3094700.085121 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3134967.531257 
## iter  10 value 3094750.167066
## iter  20 value 3094700.605759
## final  value 3094700.067319 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3332949.140137 
## final  value 3310123.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3332925.206387 
## final  value 3310123.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3329657.140116 
## final  value 3310123.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3338453.577242 
## final  value 3310123.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3324478.802375 
## final  value 3310123.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3340203.148214 
## final  value 3310123.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3345830.473838 
## final  value 3310123.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3343089.445687 
## final  value 3310123.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3322665.868620 
## final  value 3310123.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3341518.975806 
## final  value 3310123.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3329517.017764 
## final  value 3310123.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3331604.360474 
## final  value 3310123.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3330251.120303 
## final  value 3310123.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3344426.653137 
## final  value 3310123.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3323731.439992 
## final  value 3310123.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3345327.757130 
## iter  10 value 3310132.153783
## final  value 3310130.038716 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3346848.239450 
## iter  10 value 3310135.531769
## iter  10 value 3310135.510704
## iter  10 value 3310135.510612
## final  value 3310135.510612 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3347937.765088 
## iter  10 value 3310130.021896
## iter  10 value 3310129.994908
## iter  10 value 3310129.993509
## final  value 3310129.993509 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3333926.622850 
## iter  10 value 3310222.768214
## iter  20 value 3310130.343939
## final  value 3310129.986528 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3336001.004977 
## iter  10 value 3310131.745206
## final  value 3310129.990576 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3346241.355901 
## iter  10 value 3310129.122841
## final  value 3310127.329934 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3335775.680954 
## iter  10 value 3310375.835430
## final  value 3310126.907679 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3340717.857644 
## iter  10 value 3310237.263383
## iter  20 value 3310130.695709
## iter  30 value 3310127.792481
## final  value 3310126.884864 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3325815.505637 
## iter  10 value 3310254.554426
## final  value 3310126.888110 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3335948.375167 
## iter  10 value 3310343.089300
## final  value 3310128.901180 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3339788.668771 
## iter  10 value 3310141.300537
## iter  20 value 3310126.822754
## final  value 3310126.296834 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3352270.896939 
## iter  10 value 3310175.606621
## iter  20 value 3310125.907981
## final  value 3310125.751570 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3319022.351908 
## iter  10 value 3310152.075440
## iter  20 value 3310126.626432
## final  value 3310125.793622 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3346255.076622 
## iter  10 value 3310133.494471
## iter  20 value 3310126.252195
## final  value 3310125.770392 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3337601.210660 
## iter  10 value 3310158.455517
## iter  20 value 3310127.239924
## iter  30 value 3310125.856605
## iter  30 value 3310125.828548
## iter  30 value 3310125.798837
## final  value 3310125.798837 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3339110.642857 
## iter  10 value 3310195.500129
## iter  20 value 3310123.842984
## final  value 3310123.064583 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3322567.332917 
## iter  10 value 3310156.413671
## iter  20 value 3310123.389323
## final  value 3310123.063661 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3339089.698582 
## iter  10 value 3310191.389911
## iter  20 value 3310123.795372
## final  value 3310123.061102 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3337680.330519 
## iter  10 value 3310159.144259
## iter  20 value 3310123.422565
## final  value 3310123.050619 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3341110.247862 
## iter  10 value 3310158.896728
## iter  20 value 3310123.421676
## final  value 3310123.052293 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3338668.562363 
## iter  10 value 3310208.949160
## iter  20 value 3310124.009480
## final  value 3310123.085687 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3334635.266882 
## iter  10 value 3310211.313102
## iter  20 value 3310124.033814
## final  value 3310123.060515 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3325801.676616 
## iter  10 value 3310169.243783
## iter  20 value 3310123.550648
## final  value 3310123.074852 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3328794.926752 
## iter  10 value 3310255.522072
## iter  20 value 3310124.545778
## final  value 3310123.079946 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3335740.771072 
## iter  10 value 3310186.713104
## iter  20 value 3310123.749854
## final  value 3310123.065869 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3345864.086585 
## iter  10 value 3310197.482159
## iter  20 value 3310123.887420
## final  value 3310123.087019 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3337091.998026 
## iter  10 value 3310210.799669
## iter  20 value 3310124.040893
## final  value 3310123.109876 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3348136.987634 
## iter  10 value 3310239.174400
## iter  20 value 3310124.365611
## final  value 3310123.080784 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3355447.814999 
## iter  10 value 3310242.771895
## iter  20 value 3310124.409087
## final  value 3310123.115855 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3345445.108574 
## iter  10 value 3310232.969846
## iter  20 value 3310124.299304
## final  value 3310123.087422 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3150359.382642 
## final  value 3121031.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3150555.706801 
## final  value 3121031.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3147202.494481 
## final  value 3121031.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3152912.367332 
## final  value 3121031.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3148864.882545 
## final  value 3121031.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3140451.799375 
## final  value 3121031.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3151987.751098 
## final  value 3121031.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3161111.080686 
## final  value 3121031.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3138480.428990 
## final  value 3121031.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3161000.144494 
## final  value 3121031.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3136770.412580 
## final  value 3121031.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3138173.305168 
## final  value 3121031.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3144976.034236 
## final  value 3121031.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3143370.686679 
## final  value 3121031.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3151851.130376 
## final  value 3121031.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3140198.597173 
## iter  10 value 3121043.643823
## final  value 3121043.459266 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3149972.572417 
## iter  10 value 3121050.738277
## final  value 3121043.492793 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3138819.772913 
## iter  10 value 3121051.337960
## final  value 3121037.973662 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3151298.666040 
## iter  10 value 3121113.639000
## iter  20 value 3121038.191393
## final  value 3121037.972048 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3159535.309249 
## iter  10 value 3121044.193418
## final  value 3121037.992896 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3152154.365494 
## iter  10 value 3121111.874339
## iter  20 value 3121043.039883
## iter  30 value 3121040.843759
## iter  40 value 3121037.236210
## final  value 3121036.167288 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3139857.694787 
## iter  10 value 3121067.813448
## iter  20 value 3121035.045152
## final  value 3121034.912239 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3145302.868939 
## iter  10 value 3121057.922311
## iter  20 value 3121040.162425
## iter  30 value 3121034.975844
## final  value 3121034.882067 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3148744.660281 
## iter  10 value 3121108.547843
## final  value 3121038.169920 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3152053.434351 
## iter  10 value 3121178.113779
## iter  20 value 3121038.480767
## final  value 3121034.899331 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3147556.218610 
## iter  10 value 3121042.457625
## iter  10 value 3121042.451642
## final  value 3121035.981885 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3150556.858918 
## iter  10 value 3121243.480130
## iter  20 value 3121033.814549
## iter  20 value 3121033.784447
## iter  20 value 3121033.766454
## final  value 3121033.766454 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3155444.024134 
## iter  10 value 3121381.567675
## final  value 3121033.768555 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3155804.994215 
## iter  10 value 3121946.829494
## iter  20 value 3121040.470785
## iter  30 value 3121039.664773
## iter  40 value 3121035.217143
## final  value 3121034.234495 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3161413.852826 
## iter  10 value 3121232.001966
## final  value 3121034.701977 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3155941.770752 
## iter  10 value 3121062.743885
## iter  20 value 3121031.371513
## final  value 3121031.044853 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3143107.228404 
## iter  10 value 3121087.888196
## iter  20 value 3121031.663353
## final  value 3121031.052586 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3159692.315859 
## final  value 3121056.118651 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3142403.727691 
## iter  10 value 3121093.470941
## iter  20 value 3121031.723969
## final  value 3121031.053228 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3156611.640343 
## iter  10 value 3121064.426293
## iter  20 value 3121031.391658
## final  value 3121031.047688 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3139276.011389 
## iter  10 value 3121090.410207
## iter  20 value 3121031.700567
## final  value 3121031.062786 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3148261.792589 
## iter  10 value 3121136.054342
## iter  20 value 3121032.231783
## final  value 3121031.073996 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3153526.049194 
## iter  10 value 3121141.552468
## iter  20 value 3121032.286798
## final  value 3121031.093001 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3147575.563753 
## iter  10 value 3121138.588466
## iter  20 value 3121032.256022
## final  value 3121031.138431 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3142686.539233 
## iter  10 value 3121094.937571
## iter  20 value 3121031.756829
## final  value 3121031.072502 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3145981.252410 
## iter  10 value 3121201.674862
## iter  20 value 3121032.997928
## final  value 3121031.112924 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3144568.548997 
## final  value 3121037.862819 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3144707.695349 
## final  value 3121041.687269 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3138648.889565 
## iter  10 value 3121134.578308
## iter  20 value 3121032.223111
## final  value 3121031.081659 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3168006.246175 
## iter  10 value 3121078.147535
## iter  20 value 3121031.569823
## final  value 3121031.116159 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3176288.138753 
## final  value 3138726.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3172811.196566 
## final  value 3138726.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3170889.838128 
## final  value 3138726.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3163964.090443 
## final  value 3138726.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3161754.061451 
## final  value 3138726.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3170978.226527 
## final  value 3138726.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3168359.582625 
## final  value 3138726.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3154879.066006 
## final  value 3138726.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3169795.224029 
## final  value 3138726.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3180762.978367 
## final  value 3138726.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3174369.677375 
## final  value 3138726.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3156513.437842 
## final  value 3138726.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3161954.073686 
## final  value 3138726.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3169133.686956 
## final  value 3138726.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3169105.401654 
## final  value 3138726.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3161005.261281 
## iter  10 value 3138743.787116
## final  value 3138733.091247 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3170985.389433 
## iter  10 value 3138737.163684
## final  value 3138732.984423 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3166505.423809 
## iter  10 value 3138733.379482
## final  value 3138732.984548 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3168941.352342 
## iter  10 value 3138792.665810
## final  value 3138738.487255 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3154786.766321 
## iter  10 value 3138744.230380
## iter  20 value 3138733.285782
## final  value 3138733.030762 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3148524.175300 
## iter  10 value 3138787.362373
## final  value 3138731.069665 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3169698.133566 
## iter  10 value 3138758.323688
## final  value 3138729.904910 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3159692.791536 
## iter  10 value 3138760.222372
## iter  20 value 3138730.212926
## final  value 3138729.926208 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3179182.659280 
## iter  10 value 3138839.222870
## iter  20 value 3138731.932675
## final  value 3138729.899567 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3164406.700334 
## iter  10 value 3138734.966794
## final  value 3138730.982165 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3175397.319575 
## iter  10 value 3139029.923098
## iter  20 value 3138730.103596
## iter  30 value 3138729.018568
## iter  30 value 3138728.993577
## final  value 3138728.760601 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3170778.532583 
## iter  10 value 3138745.894584
## iter  20 value 3138729.501027
## final  value 3138728.819177 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3170227.589847 
## iter  10 value 3139270.426068
## iter  20 value 3138750.922421
## iter  30 value 3138730.819159
## final  value 3138728.764736 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3155457.572136 
## iter  10 value 3138764.203009
## iter  20 value 3138729.850928
## iter  20 value 3138729.831476
## final  value 3138729.231688 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3166815.128572 
## iter  10 value 3138767.427504
## iter  20 value 3138733.336387
## iter  30 value 3138728.935621
## final  value 3138728.848475 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3160205.547252 
## iter  10 value 3138787.898752
## iter  20 value 3138726.718318
## final  value 3138726.053729 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3156488.660760 
## iter  10 value 3138778.428020
## iter  20 value 3138726.609553
## final  value 3138726.051215 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3167075.843311 
## iter  10 value 3138761.520218
## iter  20 value 3138726.414086
## final  value 3138726.048553 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3166306.807204 
## iter  10 value 3138761.715211
## iter  20 value 3138726.416865
## final  value 3138726.049328 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3162982.068578 
## iter  10 value 3138780.088364
## iter  20 value 3138726.628304
## final  value 3138726.047577 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3158267.532061 
## iter  10 value 3138813.698667
## iter  20 value 3138727.030480
## final  value 3138726.063984 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3163444.470445 
## final  value 3138726.035902 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3165331.659921 
## iter  10 value 3138803.094819
## iter  20 value 3138726.905871
## final  value 3138726.056293 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3179744.885766 
## iter  10 value 3138796.809539
## iter  20 value 3138726.833272
## final  value 3138726.074407 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3154502.568715 
## iter  10 value 3138772.798471
## iter  20 value 3138726.555912
## final  value 3138726.053554 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3179296.181747 
## iter  10 value 3138839.720565
## iter  20 value 3138727.335368
## final  value 3138726.118674 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3160184.830009 
## final  value 3138726.062334 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3171902.681945 
## iter  10 value 3138851.220805
## iter  20 value 3138727.469232
## final  value 3138726.100281 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3154460.518696 
## final  value 3138726.038302 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3172878.808839 
## iter  10 value 3138871.598158
## iter  20 value 3138727.706172
## final  value 3138726.193803 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3228881.367917 
## final  value 3200761.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3234138.946862 
## final  value 3200761.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3220668.378547 
## final  value 3200761.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3224204.369119 
## final  value 3200761.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3224205.152094 
## final  value 3200761.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3232221.921947 
## final  value 3200761.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3224321.008188 
## final  value 3200761.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3247401.394433 
## final  value 3200761.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3220734.791259 
## final  value 3200761.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3222458.100356 
## final  value 3200761.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3236631.888239 
## final  value 3200761.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3232509.540971 
## final  value 3200761.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3232601.397092 
## final  value 3200761.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3228739.710448 
## final  value 3200761.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3212035.392818 
## final  value 3200761.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3220147.414808 
## iter  10 value 3200931.709701
## iter  20 value 3200768.796443
## final  value 3200767.974764 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3231611.713633 
## iter  10 value 3200771.469925
## final  value 3200767.974311 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3214083.151416 
## iter  10 value 3200852.782432
## iter  20 value 3200767.997904
## iter  20 value 3200767.982939
## iter  20 value 3200767.982386
## final  value 3200767.982386 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3229622.855728 
## iter  10 value 3200772.907635
## final  value 3200767.976192 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3219432.836825 
## iter  10 value 3200788.701657
## iter  20 value 3200768.159036
## final  value 3200767.996952 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3233030.545832 
## iter  10 value 3200847.775966
## iter  20 value 3200765.032816
## iter  20 value 3200765.001948
## iter  20 value 3200764.999914
## final  value 3200764.999914 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3230764.908923 
## iter  10 value 3200937.042036
## iter  20 value 3200766.548188
## iter  30 value 3200765.106157
## final  value 3200764.898504 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3217834.918432 
## iter  10 value 3200876.040321
## iter  20 value 3200772.856283
## iter  30 value 3200766.137511
## final  value 3200765.973222 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3231631.987563 
## iter  10 value 3200823.506062
## iter  20 value 3200766.650837
## final  value 3200765.150437 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3229575.361228 
## iter  10 value 3200865.789983
## iter  20 value 3200767.260188
## final  value 3200765.970240 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3240546.840508 
## iter  10 value 3200789.238206
## iter  20 value 3200764.872765
## final  value 3200763.817348 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3235214.175299 
## iter  10 value 3200839.769326
## iter  20 value 3200764.785406
## final  value 3200763.800226 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3228107.846239 
## iter  10 value 3201184.028787
## iter  20 value 3200765.833446
## final  value 3200764.244424 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3229808.257773 
## iter  10 value 3201183.978380
## iter  20 value 3200766.775184
## iter  30 value 3200763.776631
## iter  30 value 3200763.764624
## iter  30 value 3200763.756409
## final  value 3200763.756409 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3220931.197596 
## iter  10 value 3201108.346654
## iter  20 value 3200764.625004
## final  value 3200763.797971 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3223674.189187 
## iter  10 value 3200804.412905
## iter  20 value 3200761.506460
## final  value 3200761.059707 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3230309.538840 
## iter  10 value 3200919.075205
## iter  20 value 3200762.827630
## final  value 3200761.056460 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3224585.966351 
## iter  10 value 3200802.728606
## iter  20 value 3200761.487140
## final  value 3200761.057722 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3223176.822534 
## iter  10 value 3200825.691245
## iter  20 value 3200761.751588
## final  value 3200761.057023 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3234042.632083 
## iter  10 value 3200794.279857
## iter  20 value 3200761.388810
## final  value 3200761.046338 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3226642.439619 
## iter  10 value 3200933.105759
## iter  20 value 3200763.003272
## final  value 3200761.074999 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3236237.186466 
## iter  10 value 3200851.192742
## iter  20 value 3200762.054507
## final  value 3200761.077580 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3226708.343307 
## iter  10 value 3200835.296508
## iter  20 value 3200761.872523
## final  value 3200761.070298 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3231156.379165 
## iter  10 value 3200839.155238
## iter  20 value 3200761.918575
## final  value 3200761.074690 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3231005.140806 
## iter  10 value 3200835.585284
## iter  20 value 3200761.873975
## final  value 3200761.051978 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3227499.002266 
## iter  10 value 3200876.107960
## iter  20 value 3200762.354638
## final  value 3200761.081519 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3226614.865208 
## final  value 3200763.024448 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3240384.431819 
## iter  10 value 3200861.879366
## iter  20 value 3200762.193426
## final  value 3200761.115628 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3229419.696034 
## iter  10 value 3200890.308950
## iter  20 value 3200762.518308
## final  value 3200761.069795 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3228821.602014 
## iter  10 value 3200886.234554
## iter  20 value 3200762.466715
## final  value 3200761.064336 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3098676.513508 
## final  value 3065405.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3081468.646555 
## final  value 3065405.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3080078.874815 
## final  value 3065405.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3100290.633765 
## final  value 3065405.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3100942.475858 
## final  value 3065405.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3099609.280729 
## final  value 3065405.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3083550.442142 
## final  value 3065405.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3109173.086520 
## final  value 3065405.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3089876.035128 
## final  value 3065405.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3095856.303066 
## final  value 3065405.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3091022.328728 
## final  value 3065405.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3086291.933883 
## final  value 3065405.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3078483.505925 
## final  value 3065405.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3085644.705848 
## final  value 3065405.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3096244.603885 
## final  value 3065405.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3084159.639847 
## iter  10 value 3065474.239515
## final  value 3065411.973616 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3087275.173862 
## iter  10 value 3065426.882074
## iter  20 value 3065412.051047
## final  value 3065411.984267 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3092385.794173 
## iter  10 value 3065480.414036
## final  value 3065411.968427 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3090662.135729 
## iter  10 value 3065564.879062
## iter  20 value 3065412.045981
## final  value 3065411.993073 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3091414.000544 
## iter  10 value 3065477.865081
## iter  20 value 3065412.075562
## final  value 3065411.966854 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3094259.152634 
## iter  10 value 3065468.357923
## iter  20 value 3065416.875193
## iter  30 value 3065410.147341
## final  value 3065410.038973 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3092789.961044 
## iter  10 value 3065940.416505
## iter  20 value 3065410.943477
## final  value 3065409.306499 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3092915.979347 
## iter  10 value 3065450.293263
## iter  20 value 3065409.432650
## final  value 3065408.914412 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3106194.070095 
## iter  10 value 3065425.832183
## iter  20 value 3065408.880767
## iter  20 value 3065408.878081
## iter  20 value 3065408.877242
## final  value 3065408.877242 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3094379.817084 
## iter  10 value 3065418.035631
## iter  20 value 3065410.875834
## final  value 3065408.889250 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3078957.171262 
## iter  10 value 3065464.617379
## iter  20 value 3065408.470002
## final  value 3065407.763354 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3093534.334078 
## iter  10 value 3065455.056902
## iter  20 value 3065411.978577
## final  value 3065409.967250 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3096515.492509 
## iter  10 value 3065411.523990
## final  value 3065409.957914 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3090233.202630 
## iter  10 value 3065409.319181
## final  value 3065407.786259 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3096213.175264 
## iter  10 value 3065609.067604
## final  value 3065409.737068 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3097825.821540 
## iter  10 value 3065455.105201
## iter  20 value 3065405.582741
## final  value 3065405.044786 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3089280.440209 
## iter  10 value 3065464.914568
## iter  20 value 3065405.695592
## final  value 3065405.052308 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3097278.988769 
## iter  10 value 3065468.867408
## iter  20 value 3065405.746231
## final  value 3065405.060545 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3103559.832913 
## iter  10 value 3065454.128563
## iter  20 value 3065405.570978
## final  value 3065405.060627 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3089857.546772 
## iter  10 value 3065472.514804
## iter  20 value 3065405.781424
## final  value 3065405.056520 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3095742.701236 
## iter  10 value 3065513.776241
## iter  20 value 3065406.272069
## final  value 3065405.053386 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3091056.632636 
## iter  10 value 3065528.638027
## iter  20 value 3065406.441469
## final  value 3065405.106387 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3095865.713625 
## iter  10 value 3065527.476121
## iter  20 value 3065406.429378
## final  value 3065405.106855 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3087054.406495 
## iter  10 value 3065493.443820
## iter  20 value 3065406.035749
## final  value 3065405.117052 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3080915.647234 
## final  value 3065405.065682 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3093941.276578 
## iter  10 value 3065525.143891
## iter  20 value 3065406.421368
## final  value 3065405.075453 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3091409.379216 
## final  value 3065405.053352 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3109799.213927 
## final  value 3065406.153623 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3097769.007771 
## final  value 3065405.102021 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3088432.797342 
## iter  10 value 3065514.500910
## iter  20 value 3065406.283367
## final  value 3065405.072223 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3102362.541855 
## final  value 3083602.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3104270.129607 
## final  value 3083602.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3111902.066041 
## final  value 3083602.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3119459.383183 
## final  value 3083602.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3111231.528532 
## final  value 3083602.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3111339.168455 
## final  value 3083602.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3124925.553435 
## final  value 3083602.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3122230.582474 
## final  value 3083602.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3124431.432963 
## final  value 3083602.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3119305.614326 
## final  value 3083602.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3118873.528735 
## final  value 3083602.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3119388.725727 
## final  value 3083602.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3113426.934286 
## final  value 3083602.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3102887.783818 
## final  value 3083602.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3097848.670028 
## final  value 3083602.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3116529.335490 
## iter  10 value 3083615.920889
## iter  20 value 3083609.051354
## final  value 3083608.985624 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3098129.832953 
## iter  10 value 3083611.663568
## final  value 3083608.968663 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3103308.668247 
## iter  10 value 3083609.183926
## final  value 3083608.971946 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3119640.833757 
## iter  10 value 3083612.142358
## final  value 3083609.032978 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3104252.430037 
## iter  10 value 3083636.717216
## iter  20 value 3083609.056344
## final  value 3083608.996428 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3111961.379708 
## iter  10 value 3083704.398433
## iter  20 value 3083606.062559
## final  value 3083605.893042 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3107486.603951 
## iter  10 value 3084092.368428
## final  value 3083606.946693 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3126029.260198 
## iter  10 value 3083618.872017
## iter  20 value 3083607.382137
## final  value 3083605.898811 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3114540.774466 
## iter  10 value 3083820.488141
## iter  20 value 3083605.968444
## iter  20 value 3083605.941962
## iter  20 value 3083605.911903
## final  value 3083605.911903 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3103709.631633 
## iter  10 value 3083677.498298
## iter  20 value 3083607.089788
## iter  20 value 3083607.063444
## iter  20 value 3083607.046508
## final  value 3083607.046508 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3106745.337141 
## iter  10 value 3084283.144749
## iter  20 value 3083615.485205
## iter  30 value 3083605.555845
## final  value 3083605.383574 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3101260.790238 
## iter  10 value 3083962.089910
## iter  20 value 3083652.571541
## iter  30 value 3083626.175916
## iter  40 value 3083606.876505
## final  value 3083605.219636 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3123667.250772 
## iter  10 value 3083629.765697
## iter  20 value 3083605.271879
## final  value 3083605.223520 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3124114.550888 
## iter  10 value 3083839.967227
## iter  20 value 3083612.668839
## iter  30 value 3083605.340707
## final  value 3083605.248183 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3104549.814035 
## iter  10 value 3083816.091734
## iter  20 value 3083605.424911
## final  value 3083604.879271 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3117752.367934 
## iter  10 value 3083633.853384
## iter  20 value 3083602.373508
## final  value 3083602.045727 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3110522.046270 
## iter  10 value 3083648.414548
## iter  20 value 3083602.540209
## final  value 3083602.041882 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3110851.348894 
## iter  10 value 3083636.852295
## iter  20 value 3083602.406889
## final  value 3083602.048234 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3107956.373078 
## iter  10 value 3083669.275607
## iter  20 value 3083602.781188
## final  value 3083602.058873 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3113160.580568 
## iter  10 value 3083639.564535
## iter  20 value 3083602.438230
## final  value 3083602.051661 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3112045.184898 
## iter  10 value 3083695.233094
## iter  20 value 3083603.093596
## final  value 3083602.066458 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3097748.188604 
## iter  10 value 3083679.097126
## iter  20 value 3083602.906731
## final  value 3083602.105927 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3117657.789547 
## final  value 3083604.124143 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3108011.913448 
## iter  10 value 3083652.684904
## iter  20 value 3083602.601333
## final  value 3083602.057250 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3113073.376269 
## iter  10 value 3083696.307685
## iter  20 value 3083603.104767
## final  value 3083602.081913 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3117068.586590 
## iter  10 value 3083694.256070
## iter  20 value 3083603.090106
## final  value 3083602.085018 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3106580.130927 
## iter  10 value 3083688.776247
## iter  20 value 3083603.028090
## final  value 3083602.074557 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3127993.466130 
## iter  10 value 3083680.293891
## iter  20 value 3083602.929446
## final  value 3083602.164283 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3109279.471386 
## final  value 3083602.048913 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3114062.095536 
## final  value 3083602.039377 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3203280.507492 
## final  value 3174445.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3205676.100885 
## final  value 3174445.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3207081.013497 
## final  value 3174445.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3203721.980155 
## final  value 3174445.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3197459.915073 
## final  value 3174445.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3196117.204728 
## final  value 3174445.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3205838.816036 
## final  value 3174445.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3203232.813397 
## final  value 3174445.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3211947.915494 
## final  value 3174445.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3208405.085896 
## final  value 3174445.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3209466.402908 
## final  value 3174445.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3206263.118386 
## final  value 3174445.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3194127.807583 
## final  value 3174445.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3190497.268278 
## final  value 3174445.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3220094.214953 
## final  value 3174445.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3212429.937380 
## iter  10 value 3174470.672223
## iter  20 value 3174452.006541
## iter  20 value 3174451.997409
## iter  20 value 3174451.997409
## final  value 3174451.997409 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3209958.940503 
## iter  10 value 3174453.070379
## iter  10 value 3174453.057636
## final  value 3174452.338280 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3203184.949312 
## iter  10 value 3174640.603866
## iter  20 value 3174454.711593
## final  value 3174451.992466 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3191979.315280 
## iter  10 value 3174618.271164
## final  value 3174452.006734 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3203143.187776 
## iter  10 value 3174468.459340
## final  value 3174452.002705 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3202796.307810 
## iter  10 value 3174461.555982
## iter  20 value 3174449.905718
## iter  20 value 3174449.896574
## final  value 3174449.027604 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3207964.631494 
## iter  10 value 3174700.197513
## iter  20 value 3174451.156792
## final  value 3174448.922728 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3194888.097607 
## iter  10 value 3174657.191989
## iter  20 value 3174453.811585
## final  value 3174448.906899 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3190537.775751 
## iter  10 value 3174458.512018
## final  value 3174449.238961 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3209981.905440 
## iter  10 value 3174466.172821
## iter  20 value 3174450.807616
## iter  30 value 3174449.142445
## final  value 3174448.913621 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3197949.080639 
## iter  10 value 3174554.005442
## iter  20 value 3174450.539036
## final  value 3174447.785184 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3193132.735903 
## iter  10 value 3174695.899578
## iter  20 value 3174448.297379
## final  value 3174447.819616 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3195578.824736 
## iter  10 value 3174468.926191
## iter  20 value 3174451.611574
## iter  30 value 3174449.131481
## final  value 3174448.936645 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3218821.121360 
## iter  10 value 3174684.296751
## iter  20 value 3174448.081176
## final  value 3174447.781741 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3218501.710278 
## iter  10 value 3174475.955464
## iter  20 value 3174449.015708
## final  value 3174447.771654 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3200247.188459 
## final  value 3174445.273760 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3199655.801468 
## iter  10 value 3174512.244342
## iter  20 value 3174445.782136
## final  value 3174445.060167 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3193990.556304 
## iter  10 value 3174501.684322
## iter  20 value 3174445.658809
## final  value 3174445.050214 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3198680.808638 
## iter  10 value 3174480.005987
## iter  20 value 3174445.409518
## final  value 3174445.049287 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3194967.612951 
## iter  10 value 3174475.300914
## iter  20 value 3174445.354196
## final  value 3174445.042381 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3218328.113631 
## iter  10 value 3174539.373721
## iter  20 value 3174446.102614
## final  value 3174445.122301 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3200888.287407 
## iter  10 value 3174569.461415
## iter  20 value 3174446.452051
## final  value 3174445.159183 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3215203.659533 
## iter  10 value 3174554.026630
## iter  20 value 3174446.273071
## final  value 3174445.071463 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3203323.468641 
## iter  10 value 3174552.341705
## iter  20 value 3174446.256330
## final  value 3174445.073315 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3211106.321156 
## final  value 3174472.048483 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3180152.192504 
## iter  10 value 3174465.543514
## iter  20 value 3174445.263674
## final  value 3174445.066748 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3202379.866990 
## iter  10 value 3174557.713921
## iter  20 value 3174446.326875
## final  value 3174445.111552 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3199562.231861 
## final  value 3174445.043767 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3215370.477797 
## iter  10 value 3174598.882405
## iter  20 value 3174446.804497
## final  value 3174445.304792 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3213050.407511 
## iter  10 value 3174576.220981
## iter  20 value 3174446.542867
## final  value 3174445.213415 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3381237.255139 
## final  value 3344780.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3363441.915235 
## final  value 3344780.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3368838.072770 
## final  value 3344780.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3380132.002979 
## final  value 3344780.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3373324.341569 
## final  value 3344780.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3376195.495196 
## final  value 3344780.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3370700.807464 
## final  value 3344780.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3361231.678905 
## final  value 3344780.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3364654.923978 
## final  value 3344780.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3360593.640370 
## final  value 3344780.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3359838.997136 
## final  value 3344780.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3373364.821789 
## final  value 3344780.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3390534.467210 
## final  value 3344780.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3381476.388260 
## final  value 3344780.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3368263.673256 
## final  value 3344780.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3362578.788591 
## iter  10 value 3344815.793153
## final  value 3344786.992333 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3368852.783471 
## iter  10 value 3344847.771953
## final  value 3344792.510057 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3365564.640096 
## iter  10 value 3344787.449374
## final  value 3344786.994978 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3369071.659795 
## iter  10 value 3344825.462075
## iter  20 value 3344787.189602
## final  value 3344787.022833 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3370205.565201 
## iter  10 value 3344787.748782
## final  value 3344787.001143 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3370717.076516 
## iter  10 value 3344794.723636
## iter  20 value 3344784.138490
## final  value 3344783.898485 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3378598.243852 
## iter  10 value 3344998.964271
## iter  20 value 3344785.677124
## iter  30 value 3344784.007033
## iter  30 value 3344784.001635
## iter  30 value 3344783.988026
## final  value 3344783.988026 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3387241.963902 
## iter  10 value 3344910.058674
## iter  20 value 3344784.739446
## final  value 3344783.913549 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3369841.643316 
## iter  10 value 3345108.108471
## iter  20 value 3344800.760857
## final  value 3344783.986914 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3374631.783214 
## iter  10 value 3344817.902309
## iter  20 value 3344785.078302
## final  value 3344783.986163 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3373895.617580 
## iter  10 value 3345123.836506
## iter  20 value 3344789.126291
## iter  30 value 3344784.524149
## final  value 3344783.252427 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3382472.216983 
## iter  10 value 3344789.693916
## iter  20 value 3344782.879446
## iter  20 value 3344782.865927
## iter  20 value 3344782.850966
## final  value 3344782.850966 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3372451.239280 
## iter  10 value 3344995.639467
## iter  20 value 3344784.934752
## final  value 3344784.726926 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3376768.476328 
## iter  10 value 3344838.293295
## iter  20 value 3344785.846693
## final  value 3344783.248611 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3382864.065911 
## iter  10 value 3344977.890259
## iter  20 value 3344783.204175
## final  value 3344782.825017 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3376049.455871 
## iter  10 value 3344857.872511
## iter  20 value 3344780.903795
## final  value 3344780.062871 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3373822.694879 
## iter  10 value 3344816.711046
## iter  20 value 3344780.425727
## final  value 3344780.047921 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3374660.602949 
## iter  10 value 3344853.256629
## iter  20 value 3344780.850946
## final  value 3344780.064417 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3367416.345708 
## iter  10 value 3344844.996229
## iter  20 value 3344780.755145
## final  value 3344780.057305 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3368388.069732 
## iter  10 value 3344874.309133
## iter  20 value 3344781.091390
## final  value 3344780.072945 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3387036.329664 
## final  value 3344781.403884 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3386109.116091 
## iter  10 value 3344855.360101
## iter  20 value 3344780.886078
## final  value 3344780.103315 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3364094.954871 
## iter  10 value 3344836.928226
## iter  20 value 3344780.673183
## final  value 3344780.062062 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3369748.436177 
## iter  10 value 3344879.189847
## iter  20 value 3344781.160784
## final  value 3344780.089736 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3368702.083511 
## iter  10 value 3344916.279848
## iter  20 value 3344781.590061
## final  value 3344780.142752 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3374968.504541 
## iter  10 value 3344926.708990
## iter  20 value 3344781.717336
## final  value 3344780.073683 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3357165.300939 
## iter  10 value 3344832.428829
## iter  20 value 3344780.630417
## final  value 3344780.172112 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3384585.831584 
## iter  10 value 3344891.947708
## iter  20 value 3344781.318290
## final  value 3344780.155509 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3358051.685700 
## iter  10 value 3344857.088778
## iter  20 value 3344780.918071
## final  value 3344780.172351 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3372845.973397 
## iter  10 value 3344984.208337
## iter  20 value 3344782.384342
## final  value 3344780.091259 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3090415.012131 
## final  value 3068417.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3096623.555612 
## final  value 3068417.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3105380.579945 
## final  value 3068417.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3094982.078727 
## final  value 3068417.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3098641.038643 
## final  value 3068417.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3093792.836107 
## final  value 3068417.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3101858.120400 
## final  value 3068417.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3097494.749635 
## final  value 3068417.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3090743.861361 
## final  value 3068417.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3111682.365417 
## final  value 3068417.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3089411.209620 
## final  value 3068417.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3107501.078387 
## final  value 3068417.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3104891.146192 
## final  value 3068417.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3091001.785455 
## final  value 3068417.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3105009.869363 
## final  value 3068417.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3098099.779778 
## iter  10 value 3068493.612337
## final  value 3068423.977658 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3091693.525301 
## iter  10 value 3068456.064830
## iter  20 value 3068423.984903
## iter  20 value 3068423.968812
## iter  20 value 3068423.967324
## final  value 3068423.967324 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3095594.754216 
## iter  10 value 3068565.613956
## iter  20 value 3068424.604539
## final  value 3068423.978626 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3111691.919186 
## iter  10 value 3068977.567323
## iter  20 value 3068427.792152
## final  value 3068423.988400 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3093850.152083 
## iter  10 value 3068444.783384
## iter  20 value 3068424.022678
## iter  20 value 3068424.001865
## iter  20 value 3068424.000559
## final  value 3068424.000559 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3085955.316850 
## iter  10 value 3068624.490314
## final  value 3068421.979167 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3086502.673473 
## iter  10 value 3068540.978697
## iter  20 value 3068421.884026
## iter  30 value 3068420.889176
## iter  30 value 3068420.882795
## iter  30 value 3068420.881549
## final  value 3068420.881549 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3090691.524887 
## iter  10 value 3068655.957230
## iter  20 value 3068421.461420
## final  value 3068420.999382 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3094137.311221 
## iter  10 value 3068428.234242
## final  value 3068422.113239 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3090971.767453 
## iter  10 value 3068439.503594
## iter  20 value 3068421.270477
## iter  20 value 3068421.257767
## final  value 3068420.888542 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3106602.862243 
## iter  10 value 3068494.105542
## iter  20 value 3068421.170578
## final  value 3068419.796967 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3081164.440915 
## iter  10 value 3068603.861728
## iter  20 value 3068420.451751
## final  value 3068419.810351 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3116171.958192 
## iter  10 value 3068467.703967
## iter  20 value 3068420.542941
## final  value 3068419.967136 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3098716.769316 
## iter  10 value 3068427.280956
## iter  20 value 3068420.015341
## final  value 3068419.780245 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3098152.325952 
## iter  10 value 3068521.648718
## iter  20 value 3068424.183419
## final  value 3068424.040378 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3091692.504754 
## iter  10 value 3068478.811124
## iter  20 value 3068417.716459
## final  value 3068417.052803 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3095575.159037 
## iter  10 value 3068550.272703
## iter  20 value 3068418.542984
## final  value 3068417.143517 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3088641.471628 
## iter  10 value 3068446.648832
## iter  20 value 3068417.347659
## final  value 3068417.042563 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3100919.625964 
## iter  10 value 3068450.443437
## iter  20 value 3068417.391361
## final  value 3068417.047211 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3101552.690963 
## iter  10 value 3068451.206459
## iter  20 value 3068417.399364
## final  value 3068417.047355 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3101041.819190 
## final  value 3068466.386754 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3111221.686878 
## iter  10 value 3068473.409042
## iter  20 value 3068417.669113
## final  value 3068417.083241 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3107953.434505 
## iter  10 value 3068511.598490
## iter  20 value 3068418.109914
## final  value 3068417.127304 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3106196.807240 
## iter  10 value 3068522.412289
## iter  20 value 3068418.229940
## final  value 3068417.068159 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3105072.081085 
## iter  10 value 3068471.197159
## iter  20 value 3068417.643220
## final  value 3068417.061436 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3103870.318518 
## iter  10 value 3068418.409764
## final  value 3068417.039662 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3082421.236779 
## final  value 3068417.051709 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3110055.791845 
## iter  10 value 3068466.689205
## iter  20 value 3068417.597800
## final  value 3068417.064472 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3108516.121201 
## final  value 3068417.448506 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3088488.569808 
## final  value 3068417.058835 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 2993492.670522 
## final  value 2951189.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 2987839.248865 
## final  value 2951189.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 2978989.383264 
## final  value 2951189.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 2972484.549085 
## final  value 2951189.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 2973284.931048 
## final  value 2951189.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 2970673.269696 
## final  value 2951189.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 2995151.062833 
## final  value 2951189.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 2988529.756331 
## final  value 2951189.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 2973654.519712 
## final  value 2951189.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 2988240.582741 
## final  value 2951189.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 2968710.533536 
## final  value 2951189.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 2988028.363575 
## final  value 2951189.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 2975322.990621 
## final  value 2951189.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 2970810.994204 
## final  value 2951189.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 2963141.481140 
## final  value 2951189.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 2977698.488132 
## iter  10 value 2951354.828659
## final  value 2951195.974287 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 2981241.909453 
## iter  10 value 2951204.046466
## final  value 2951195.980616 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 2988575.835717 
## iter  10 value 2951197.245938
## final  value 2951195.962751 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 2977106.741443 
## iter  10 value 2951207.062072
## final  value 2951195.964272 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 2991442.208615 
## iter  10 value 2951224.364667
## final  value 2951195.965943 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 2989760.708963 
## iter  10 value 2951199.046902
## final  value 2951192.956031 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 2968376.106400 
## iter  10 value 2951282.951912
## iter  20 value 2951193.146947
## final  value 2951192.903447 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 2985068.991400 
## iter  10 value 2951195.138840
## iter  20 value 2951192.903609
## iter  20 value 2951192.901481
## iter  20 value 2951192.899519
## final  value 2951192.899519 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 2987223.119159 
## iter  10 value 2951202.398828
## iter  20 value 2951193.620613
## final  value 2951193.107405 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 2987111.159787 
## iter  10 value 2952453.206337
## iter  20 value 2951208.131274
## iter  30 value 2951192.969686
## final  value 2951192.881444 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 2982039.471682 
## iter  10 value 2951417.520520
## iter  20 value 2951192.471619
## final  value 2951191.764225 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 2983555.773414 
## iter  10 value 2951651.689900
## final  value 2951191.844979 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 2998619.903520 
## iter  10 value 2951196.089708
## iter  20 value 2951191.844968
## final  value 2951191.770330 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 2987943.709182 
## iter  10 value 2951220.020081
## iter  20 value 2951195.428822
## iter  20 value 2951195.412424
## final  value 2951191.819219 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 2991412.489172 
## iter  10 value 2951253.296839
## iter  20 value 2951192.334627
## final  value 2951191.760545 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 2980140.197099 
## iter  10 value 2951236.322806
## iter  20 value 2951189.551619
## final  value 2951189.043547 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 2985361.381083 
## iter  10 value 2951251.499927
## iter  20 value 2951189.727539
## final  value 2951189.056513 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 2974802.821113 
## iter  10 value 2951253.074284
## iter  20 value 2951189.744294
## final  value 2951189.056353 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 2986478.048761 
## iter  10 value 2951220.434963
## iter  20 value 2951189.366339
## final  value 2951189.042845 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 2973226.808720 
## iter  10 value 2951220.464757
## iter  20 value 2951189.368135
## final  value 2951189.044346 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 2980760.471115 
## final  value 2951197.717844 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 2994057.287744 
## final  value 2951223.479383 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 2972240.868456 
## iter  10 value 2951234.893098
## iter  20 value 2951189.548332
## final  value 2951189.055718 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 2994508.970135 
## iter  10 value 2951249.673866
## iter  20 value 2951189.716593
## final  value 2951189.128685 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 2973996.041706 
## iter  10 value 2951266.804769
## iter  20 value 2951189.913036
## final  value 2951189.072924 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 2975237.430079 
## iter  10 value 2951320.606855
## iter  20 value 2951190.544305
## final  value 2951189.123253 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 2981010.439968 
## final  value 2951191.615413 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 2989891.661351 
## iter  10 value 2951285.048932
## iter  20 value 2951190.137850
## final  value 2951189.100829 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 2985816.963320 
## final  value 2951189.061816 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 2977403.336143 
## final  value 2951189.051632 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3333473.593116 
## final  value 3311776.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3345481.512582 
## final  value 3311776.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3340875.086947 
## final  value 3311776.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3346862.304361 
## final  value 3311776.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3328439.991949 
## final  value 3311776.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3335672.620869 
## final  value 3311776.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3342634.325274 
## final  value 3311776.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3339846.594147 
## final  value 3311776.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3352335.385390 
## final  value 3311776.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3332547.856251 
## final  value 3311776.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3330454.428844 
## final  value 3311776.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3331291.635439 
## final  value 3311776.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3325941.856675 
## final  value 3311776.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3339625.995222 
## final  value 3311776.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3340846.584623 
## final  value 3311776.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3344608.593118 
## iter  10 value 3311788.708806
## iter  20 value 3311784.265919
## final  value 3311782.995768 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3333359.063634 
## iter  10 value 3311858.442929
## final  value 3311782.992506 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3345484.371395 
## iter  10 value 3311934.049151
## final  value 3311782.993211 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3342439.759128 
## iter  10 value 3311824.946052
## iter  20 value 3311783.008316
## iter  20 value 3311782.987277
## iter  20 value 3311782.985261
## final  value 3311782.985261 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3338319.996087 
## iter  10 value 3311806.329614
## final  value 3311782.994506 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3349447.848967 
## iter  10 value 3311782.697701
## final  value 3311779.924091 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3334578.703152 
## iter  10 value 3311910.983409
## iter  20 value 3311783.239068
## final  value 3311783.037181 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3336419.251440 
## iter  10 value 3311790.192549
## iter  20 value 3311780.158536
## final  value 3311779.922055 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3332343.333379 
## iter  10 value 3311810.620990
## iter  20 value 3311780.003363
## iter  20 value 3311779.979482
## iter  20 value 3311779.951521
## final  value 3311779.951521 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3339631.422173 
## iter  10 value 3311904.075723
## iter  20 value 3311781.096930
## final  value 3311779.929637 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3322445.133117 
## iter  10 value 3311986.564843
## iter  20 value 3311779.728901
## final  value 3311778.789499 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3322357.830866 
## iter  10 value 3311783.858424
## iter  20 value 3311779.236568
## iter  20 value 3311779.220590
## iter  20 value 3311779.204062
## final  value 3311779.204062 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3334088.581260 
## iter  10 value 3311787.773484
## iter  20 value 3311779.258610
## final  value 3311778.781141 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3332235.724204 
## iter  10 value 3311907.656858
## iter  20 value 3311781.926545
## iter  30 value 3311778.976831
## final  value 3311778.789911 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3339540.728297 
## iter  10 value 3312218.672704
## iter  20 value 3311779.272042
## final  value 3311778.777002 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3345368.945851 
## iter  10 value 3311810.187781
## iter  20 value 3311776.400437
## final  value 3311776.048630 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3341478.872266 
## iter  10 value 3311848.343001
## iter  20 value 3311776.839508
## final  value 3311776.062781 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3331658.039359 
## iter  10 value 3311810.525275
## iter  20 value 3311776.403489
## final  value 3311776.048202 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3340559.756073 
## iter  10 value 3311848.800158
## iter  20 value 3311776.844661
## final  value 3311776.063025 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3336356.479324 
## iter  10 value 3311811.091428
## iter  20 value 3311776.410431
## final  value 3311776.049321 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3350437.250449 
## iter  10 value 3311855.334470
## iter  20 value 3311776.932495
## final  value 3311776.108448 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3334932.195747 
## iter  10 value 3311886.549969
## iter  20 value 3311777.290794
## final  value 3311776.142442 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3342113.240681 
## iter  10 value 3311812.032532
## iter  20 value 3311776.427621
## final  value 3311776.056876 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3338289.718016 
## iter  10 value 3311842.267078
## iter  20 value 3311776.780856
## final  value 3311776.069461 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3330467.909883 
## iter  10 value 3311831.949688
## iter  20 value 3311776.664442
## final  value 3311776.085254 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3355492.306329 
## iter  10 value 3311851.747039
## iter  20 value 3311776.904324
## final  value 3311776.117652 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3340614.441443 
## final  value 3311776.048308 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3353224.627452 
## iter  10 value 3311947.283157
## iter  20 value 3311778.002660
## final  value 3311776.223457 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3358088.059383 
## iter  10 value 3311828.469182
## iter  20 value 3311776.633632
## final  value 3311776.093654 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3345906.279984 
## iter  10 value 3311927.121730
## iter  20 value 3311777.770234
## final  value 3311776.077153 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3195081.769995 
## final  value 3151278.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3175027.304676 
## final  value 3151278.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3191342.988843 
## final  value 3151278.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3176845.980717 
## final  value 3151278.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3174852.382508 
## final  value 3151278.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3182672.158561 
## final  value 3151278.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3176180.486292 
## final  value 3151278.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3181388.822073 
## final  value 3151278.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3183633.683289 
## final  value 3151278.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3181689.671730 
## final  value 3151278.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3188502.420880 
## final  value 3151278.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3191046.633183 
## final  value 3151278.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3195509.956021 
## final  value 3151278.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3164301.177341 
## final  value 3151278.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3180473.029694 
## final  value 3151278.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3187718.072405 
## iter  10 value 3156378.790615
## iter  20 value 3151291.470540
## final  value 3151285.189230 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3184396.511323 
## iter  10 value 3151396.162734
## iter  20 value 3151285.751157
## final  value 3151284.996677 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3181325.463941 
## iter  10 value 3151297.030963
## final  value 3151285.001865 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3173654.417826 
## iter  10 value 3151363.753621
## final  value 3151284.977528 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3183120.081064 
## iter  10 value 3151311.171172
## final  value 3151286.940203 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3160485.331296 
## iter  10 value 3151318.798406
## iter  20 value 3151283.596196
## iter  30 value 3151282.026853
## final  value 3151281.892621 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3182592.378740 
## iter  10 value 3151396.000664
## iter  20 value 3151282.490795
## final  value 3151281.895604 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3185360.697316 
## iter  10 value 3151346.845416
## final  value 3151282.202914 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3189537.511707 
## iter  10 value 3151413.555838
## iter  20 value 3151282.131822
## final  value 3151281.874664 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3175709.806081 
## iter  10 value 3151297.483754
## iter  20 value 3151282.504688
## final  value 3151281.922851 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3195542.455230 
## iter  10 value 3151347.504358
## iter  20 value 3151302.212835
## iter  30 value 3151281.443698
## final  value 3151281.357866 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3181346.650513 
## iter  10 value 3151513.942729
## iter  20 value 3151281.320147
## final  value 3151280.776997 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3190648.378617 
## iter  10 value 3151303.165101
## iter  20 value 3151281.973954
## final  value 3151281.177226 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3171964.866123 
## iter  10 value 3151318.392867
## iter  20 value 3151283.284080
## iter  30 value 3151281.322300
## final  value 3151281.242993 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3159425.604262 
## iter  10 value 3151465.442690
## iter  20 value 3151280.994179
## final  value 3151280.769399 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3179245.018574 
## iter  10 value 3151318.405188
## iter  20 value 3151278.470053
## final  value 3151278.054241 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3176521.303804 
## final  value 3151278.086547 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3177206.672026 
## iter  10 value 3151345.034523
## iter  20 value 3151278.776364
## final  value 3151278.056620 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3174067.318849 
## iter  10 value 3151342.365047
## iter  20 value 3151278.749077
## final  value 3151278.058025 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3179377.574953 
## iter  10 value 3151313.111359
## iter  20 value 3151278.410786
## final  value 3151278.049471 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3187626.678784 
## final  value 3151281.890029 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3164673.558611 
## iter  10 value 3151314.784035
## iter  20 value 3151278.438832
## final  value 3151278.060373 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3184409.595978 
## iter  10 value 3151320.809717
## iter  20 value 3151278.510083
## final  value 3151278.069623 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3181430.941418 
## iter  10 value 3151392.904759
## iter  20 value 3151279.341299
## final  value 3151278.054069 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3176616.629980 
## iter  10 value 3151345.875859
## iter  20 value 3151278.797339
## final  value 3151278.068653 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3182912.549737 
## iter  10 value 3151447.651134
## iter  20 value 3151279.981392
## final  value 3151278.080672 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3179195.330881 
## iter  10 value 3151394.060434
## iter  20 value 3151279.365142
## final  value 3151278.108288 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3168170.410493 
## iter  10 value 3151396.244427
## iter  20 value 3151279.391813
## final  value 3151278.133949 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3172102.495191 
## iter  10 value 3151368.981942
## iter  20 value 3151279.077601
## final  value 3151278.074989 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3173783.470278 
## final  value 3151278.045824 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3359101.322242 
## final  value 3330080.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3353356.587395 
## final  value 3330080.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3362300.532134 
## final  value 3330080.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3359752.101165 
## final  value 3330080.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3358377.637426 
## final  value 3330080.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3360951.697539 
## final  value 3330080.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3350630.907951 
## final  value 3330080.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3346018.690375 
## final  value 3330080.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3357523.175938 
## final  value 3330080.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3359582.246784 
## final  value 3330080.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3349378.156403 
## final  value 3330080.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3366730.675212 
## final  value 3330080.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3358459.419625 
## final  value 3330080.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3342722.918722 
## final  value 3330080.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3361078.871918 
## final  value 3330080.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3362623.099656 
## iter  10 value 3330091.103682
## final  value 3330086.991689 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3349273.218341 
## iter  10 value 3330110.253756
## final  value 3330092.568004 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3360518.586831 
## iter  10 value 3330109.784227
## iter  20 value 3330087.483337
## final  value 3330087.300738 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3362851.047253 
## iter  10 value 3330089.239681
## final  value 3330086.998171 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3351872.850407 
## iter  10 value 3330128.348993
## final  value 3330086.991999 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3352536.918607 
## iter  10 value 3330187.258328
## iter  20 value 3330084.150956
## final  value 3330083.889819 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3366704.913264 
## iter  10 value 3330107.816952
## iter  20 value 3330091.793342
## iter  30 value 3330084.961342
## iter  40 value 3330083.911450
## iter  40 value 3330083.901238
## iter  40 value 3330083.899235
## final  value 3330083.899235 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3341374.851360 
## iter  10 value 3330102.105092
## iter  20 value 3330084.005444
## final  value 3330083.889969 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3353499.178442 
## iter  10 value 3330597.478982
## iter  20 value 3330085.132718
## final  value 3330083.887849 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3347005.001133 
## iter  10 value 3330174.712193
## iter  20 value 3330084.018583
## final  value 3330083.930308 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3357064.080549 
## iter  10 value 3330916.073521
## iter  20 value 3330084.335927
## final  value 3330082.933658 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3379578.592715 
## iter  10 value 3330085.897844
## final  value 3330083.242542 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3352858.643028 
## iter  10 value 3330470.743257
## iter  20 value 3330106.470159
## iter  30 value 3330084.068212
## final  value 3330082.796589 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3360733.994429 
## iter  10 value 3330229.916607
## iter  20 value 3330087.364467
## final  value 3330083.479362 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3372339.940974 
## iter  10 value 3330578.994622
## iter  20 value 3330102.569883
## iter  30 value 3330084.263144
## iter  40 value 3330082.842874
## iter  40 value 3330082.812700
## iter  40 value 3330082.789657
## final  value 3330082.789657 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3354241.925139 
## iter  10 value 3330114.632022
## iter  20 value 3330080.405363
## final  value 3330080.048982 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3354999.113456 
## iter  10 value 3330150.305261
## iter  20 value 3330080.815909
## final  value 3330080.061061 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3369294.106451 
## iter  10 value 3330133.836048
## iter  20 value 3330080.629009
## final  value 3330080.069783 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3362091.581257 
## iter  10 value 3330177.541444
## iter  20 value 3330081.130454
## final  value 3330080.055360 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3348958.511089 
## iter  10 value 3330108.339592
## iter  20 value 3330080.331582
## final  value 3330080.059676 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3367893.295565 
## iter  10 value 3330163.261002
## iter  20 value 3330080.976286
## final  value 3330080.058722 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3361339.449540 
## iter  10 value 3330223.852613
## iter  20 value 3330081.674875
## final  value 3330080.128610 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3348781.027351 
## iter  10 value 3330173.571852
## iter  20 value 3330081.098162
## final  value 3330080.066929 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3350356.766817 
## final  value 3330080.057689 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3356817.227633 
## iter  10 value 3330255.371206
## iter  20 value 3330082.039946
## final  value 3330080.146196 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3363695.814838 
## iter  10 value 3330160.072468
## iter  20 value 3330080.950232
## final  value 3330080.085719 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3350962.644449 
## iter  10 value 3330207.460390
## iter  20 value 3330081.505143
## final  value 3330080.077243 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3360949.318913 
## iter  10 value 3330220.953558
## iter  20 value 3330081.655352
## final  value 3330080.096349 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3367486.536233 
## iter  10 value 3330207.571150
## iter  20 value 3330081.495233
## final  value 3330080.155253 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3354612.643997 
## iter  10 value 3330190.729399
## iter  20 value 3330081.303952
## final  value 3330080.113846 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3370364.150450 
## final  value 3335468.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3359377.134699 
## final  value 3335468.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3361446.877878 
## final  value 3335468.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3370064.388798 
## final  value 3335468.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3352488.628917 
## final  value 3335468.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3361801.461392 
## final  value 3335468.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3357699.400120 
## final  value 3335468.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3372388.502417 
## final  value 3335468.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3357929.916152 
## final  value 3335468.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3364489.980607 
## final  value 3335468.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3364183.986277 
## final  value 3335468.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3367737.868617 
## final  value 3335468.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3370685.380758 
## final  value 3335468.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3374024.166602 
## final  value 3335468.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3361210.489636 
## final  value 3335468.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3371816.593989 
## iter  10 value 3335480.357674
## final  value 3335474.998381 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3365214.783037 
## iter  10 value 3335484.829996
## final  value 3335475.013905 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3368628.809755 
## iter  10 value 3335585.868130
## iter  20 value 3335475.011655
## iter  20 value 3335475.002782
## iter  20 value 3335474.996093
## final  value 3335474.996093 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3359468.608079 
## iter  10 value 3335643.461317
## final  value 3335475.026385 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3364328.788480 
## iter  10 value 3335745.957047
## iter  20 value 3335479.682798
## final  value 3335475.019266 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3363741.277432 
## iter  10 value 3335478.729731
## iter  20 value 3335473.258623
## final  value 3335471.913815 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3361806.451568 
## iter  10 value 3335681.705372
## iter  20 value 3335472.768283
## final  value 3335471.916790 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3367704.728194 
## iter  10 value 3335587.724204
## iter  20 value 3335472.003923
## final  value 3335471.928819 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3367465.795063 
## iter  10 value 3335509.489562
## iter  20 value 3335473.186983
## final  value 3335471.928876 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3350934.416862 
## iter  10 value 3335663.692416
## iter  20 value 3335472.115607
## final  value 3335471.957591 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3367934.244173 
## iter  10 value 3335479.730783
## final  value 3335471.219864 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3367209.947468 
## iter  10 value 3335518.953192
## iter  20 value 3335479.335826
## final  value 3335471.277858 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3361318.523530 
## iter  10 value 3335502.524753
## iter  20 value 3335472.159517
## final  value 3335471.928741 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3360571.424433 
## iter  10 value 3335863.147203
## iter  20 value 3335472.970977
## iter  30 value 3335470.839092
## final  value 3335470.761056 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3363399.591011 
## iter  10 value 3335565.882740
## iter  20 value 3335476.634152
## iter  30 value 3335471.808105
## final  value 3335470.752289 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3361142.315726 
## iter  10 value 3335597.586790
## iter  20 value 3335469.499206
## final  value 3335468.047244 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3367435.814369 
## iter  10 value 3335503.801142
## iter  20 value 3335468.417398
## final  value 3335468.048974 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3368433.322998 
## iter  10 value 3335524.142873
## iter  20 value 3335468.653385
## final  value 3335468.050609 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3364137.006743 
## iter  10 value 3335554.277389
## iter  20 value 3335469.000090
## final  value 3335468.049148 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3374408.036155 
## iter  10 value 3335503.397378
## iter  20 value 3335468.414218
## final  value 3335468.049961 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3358974.175317 
## iter  10 value 3335546.826745
## iter  20 value 3335468.925578
## final  value 3335468.074436 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3368897.379305 
## iter  10 value 3335574.062971
## iter  20 value 3335469.239830
## final  value 3335468.094603 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3356006.485276 
## iter  10 value 3335545.067088
## iter  20 value 3335468.904460
## final  value 3335468.055122 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3363942.651804 
## iter  10 value 3335584.752784
## iter  20 value 3335469.362369
## final  value 3335468.075602 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3370381.651191 
## final  value 3335468.243581 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3374887.720381 
## iter  10 value 3335578.980026
## iter  20 value 3335469.311570
## final  value 3335468.145587 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3362340.647516 
## iter  10 value 3335596.627031
## iter  20 value 3335469.510183
## final  value 3335468.090708 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3365270.807498 
## iter  10 value 3335641.782088
## iter  20 value 3335470.030068
## final  value 3335468.153547 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3368732.109037 
## final  value 3335468.099992 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3364639.734333 
## iter  10 value 3335632.524204
## iter  20 value 3335469.924118
## final  value 3335468.104350 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3101351.666895 
## final  value 3073653.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3096901.068626 
## final  value 3073653.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3097552.019165 
## final  value 3073653.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3099907.571161 
## final  value 3073653.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3104634.712036 
## final  value 3073653.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3103505.006592 
## final  value 3073653.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3106593.268881 
## final  value 3073653.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3090552.415011 
## final  value 3073653.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3099045.314279 
## final  value 3073653.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3100669.018995 
## final  value 3073653.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3095550.666876 
## final  value 3073653.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3097804.558245 
## final  value 3073653.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3090191.337228 
## final  value 3073653.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3099943.055973 
## final  value 3073653.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3112292.000796 
## final  value 3073653.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3094247.575634 
## iter  10 value 3074927.072444
## iter  20 value 3073660.169493
## final  value 3073659.971813 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3099679.687005 
## iter  10 value 3073688.694674
## final  value 3073659.971343 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3094109.042537 
## iter  10 value 3073714.697969
## final  value 3073659.970088 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3107331.841802 
## iter  10 value 3074167.514840
## iter  20 value 3073664.930326
## final  value 3073659.967528 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3096390.772992 
## iter  10 value 3073725.227635
## iter  20 value 3073660.931022
## iter  20 value 3073660.920306
## final  value 3073659.991555 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3096538.036269 
## iter  10 value 3073666.281976
## iter  20 value 3073656.951980
## iter  20 value 3073656.943063
## final  value 3073656.882187 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3101977.818321 
## iter  10 value 3073709.224517
## iter  20 value 3073660.740600
## iter  30 value 3073656.914766
## iter  30 value 3073656.912372
## iter  30 value 3073656.911851
## final  value 3073656.911851 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3104489.634877 
## iter  10 value 3073769.302661
## iter  20 value 3073656.950515
## iter  20 value 3073656.920702
## iter  20 value 3073656.918934
## final  value 3073656.918934 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3104914.271966 
## iter  10 value 3073659.883840
## iter  20 value 3073657.949962
## final  value 3073656.948173 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3085582.742411 
## iter  10 value 3073776.832883
## iter  20 value 3073664.535334
## final  value 3073656.916360 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3109169.872003 
## iter  10 value 3073844.610965
## iter  20 value 3073655.852079
## final  value 3073655.764364 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3104649.983427 
## iter  10 value 3074030.670545
## iter  20 value 3073655.998511
## final  value 3073655.766364 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3104990.567102 
## iter  10 value 3073841.183021
## iter  20 value 3073657.190806
## iter  30 value 3073655.847577
## iter  30 value 3073655.839802
## iter  30 value 3073655.834554
## final  value 3073655.834554 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3117518.881643 
## iter  10 value 3073797.823973
## iter  20 value 3073656.779966
## final  value 3073656.237119 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3107182.569237 
## iter  10 value 3073815.025907
## iter  20 value 3073703.861305
## iter  30 value 3073658.300847
## iter  40 value 3073656.401647
## final  value 3073656.244101 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3095225.564323 
## iter  10 value 3073713.215385
## iter  20 value 3073653.699315
## final  value 3073653.052803 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3111463.074327 
## iter  10 value 3073707.650632
## iter  20 value 3073653.635452
## final  value 3073653.067740 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3105229.295773 
## iter  10 value 3073693.223969
## iter  20 value 3073653.468712
## final  value 3073653.054771 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3110510.783305 
## iter  10 value 3073689.102397
## iter  20 value 3073653.419994
## final  value 3073653.048462 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3109430.849877 
## iter  10 value 3074409.588736
## iter  20 value 3073661.722874
## iter  30 value 3073653.100568
## final  value 3073653.041172 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3095473.900026 
## iter  10 value 3073746.128710
## iter  20 value 3073654.091408
## final  value 3073653.065046 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3106404.779981 
## iter  10 value 3073748.663468
## iter  20 value 3073654.121111
## final  value 3073653.072529 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3104581.774284 
## iter  10 value 3073725.223614
## iter  20 value 3073653.851857
## final  value 3073653.057106 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3116355.340263 
## iter  10 value 3073710.260688
## iter  20 value 3073653.678520
## final  value 3073653.113754 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3106436.580741 
## iter  10 value 3073741.519305
## iter  20 value 3073654.035045
## final  value 3073653.079833 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3102377.915073 
## iter  10 value 3073729.285593
## iter  20 value 3073653.910693
## final  value 3073653.070090 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3106245.175842 
## final  value 3073653.053820 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3094792.808508 
## iter  10 value 3073769.398102
## iter  20 value 3073654.367695
## final  value 3073653.084729 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3098587.340324 
## iter  10 value 3073820.066253
## iter  20 value 3073654.956496
## final  value 3073653.084777 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3092967.801990 
## final  value 3073653.056888 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3113337.252475 
## final  value 3072883.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3096906.893041 
## final  value 3072883.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3109314.462155 
## final  value 3072883.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3093976.541271 
## final  value 3072883.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3105914.480366 
## final  value 3072883.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3098301.690919 
## final  value 3072883.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3120049.548002 
## final  value 3072883.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3114725.761143 
## final  value 3072883.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3108863.776987 
## final  value 3072883.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3112562.152495 
## final  value 3072883.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3101383.081986 
## final  value 3072883.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3102524.952296 
## final  value 3072883.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3099695.681344 
## final  value 3072883.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3109611.333595 
## final  value 3072883.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3088632.178021 
## final  value 3072883.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3093568.323721 
## iter  10 value 3073070.924140
## iter  20 value 3072892.713601
## final  value 3072890.021428 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3101079.312040 
## iter  10 value 3072891.929827
## final  value 3072889.991160 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3101931.988257 
## iter  10 value 3072898.591594
## final  value 3072889.972789 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3094766.789391 
## iter  10 value 3073008.733642
## final  value 3072889.979733 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3105642.590870 
## iter  10 value 3072966.235306
## final  value 3072889.983843 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3099427.651640 
## iter  10 value 3073479.334493
## iter  20 value 3072887.668145
## final  value 3072886.899806 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3091504.123646 
## iter  10 value 3072910.182384
## iter  20 value 3072887.537457
## final  value 3072886.917902 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3102913.012304 
## iter  10 value 3073119.708609
## iter  20 value 3072890.198005
## final  value 3072886.919615 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3098547.416247 
## iter  10 value 3072972.910230
## iter  20 value 3072891.333544
## iter  30 value 3072887.213305
## final  value 3072886.903549 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3103203.629908 
## iter  10 value 3073138.392746
## iter  20 value 3072887.614027
## final  value 3072886.927619 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3091290.496079 
## iter  10 value 3072899.657992
## iter  20 value 3072886.382978
## final  value 3072886.242791 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3087993.119237 
## iter  10 value 3073164.506442
## iter  20 value 3072889.012103
## final  value 3072885.772204 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3091801.000998 
## iter  10 value 3072911.397201
## iter  20 value 3072886.959685
## final  value 3072885.765932 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3097577.823531 
## iter  10 value 3073091.170875
## iter  20 value 3072886.032107
## final  value 3072885.767931 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3102017.531420 
## iter  10 value 3073077.066365
## final  value 3072887.506259 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3100809.622516 
## iter  10 value 3072931.851172
## iter  20 value 3072883.567840
## final  value 3072883.043346 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3092430.297863 
## iter  10 value 3072911.908788
## iter  20 value 3072883.339419
## final  value 3072883.041942 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3103682.890431 
## iter  10 value 3072952.006148
## iter  20 value 3072883.801565
## final  value 3072883.056391 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3104861.599299 
## iter  10 value 3072927.988210
## iter  20 value 3072883.526293
## final  value 3072883.043301 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3107053.083819 
## iter  10 value 3072948.035755
## iter  20 value 3072883.756290
## final  value 3072883.080699 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3117060.110487 
## iter  10 value 3073725.300831
## iter  20 value 3072892.711067
## iter  30 value 3072883.111961
## final  value 3072883.045836 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3098037.202620 
## iter  10 value 3072996.942823
## iter  20 value 3072884.327734
## final  value 3072883.144117 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3093282.520878 
## iter  10 value 3072957.544832
## iter  20 value 3072883.878858
## final  value 3072883.074214 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3092033.716549 
## iter  10 value 3072955.829029
## iter  20 value 3072883.860014
## final  value 3072883.057425 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3106572.016652 
## iter  10 value 3072946.274873
## iter  20 value 3072883.744536
## final  value 3072883.065254 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3108716.009834 
## iter  10 value 3073658.795932
## iter  20 value 3072891.944318
## iter  30 value 3072883.103121
## final  value 3072883.042217 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3098642.320477 
## iter  10 value 3073014.863187
## iter  20 value 3072884.545948
## final  value 3072883.181822 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3097807.646402 
## final  value 3072883.045766 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3100548.756335 
## final  value 3072954.844101 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3104360.429324 
## final  value 3072883.062444 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3338342.358386 
## final  value 3322581.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3350946.595559 
## final  value 3322581.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3344024.140414 
## final  value 3322581.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3360264.453197 
## final  value 3322581.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3349389.078756 
## final  value 3322581.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3357698.336176 
## final  value 3322581.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3340598.472492 
## final  value 3322581.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3358243.732260 
## final  value 3322581.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3357875.886772 
## final  value 3322581.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3359473.280944 
## final  value 3322581.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3359613.215769 
## final  value 3322581.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3361353.500705 
## final  value 3322581.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3358492.144970 
## final  value 3322581.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3354296.288621 
## final  value 3322581.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3364842.186365 
## final  value 3322581.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3336364.977530 
## iter  10 value 3322673.028125
## final  value 3322587.991544 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3359004.921104 
## iter  10 value 3322597.876960
## final  value 3322588.036633 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3352163.628309 
## iter  10 value 3322595.964556
## final  value 3322588.017703 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3352900.973137 
## iter  10 value 3322593.680127
## final  value 3322588.013462 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3352132.482001 
## iter  10 value 3322616.015904
## iter  20 value 3322588.419417
## final  value 3322588.004048 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3357494.707138 
## iter  10 value 3322641.657650
## final  value 3322584.998758 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3362609.443879 
## iter  10 value 3322589.326574
## iter  20 value 3322585.174171
## final  value 3322585.103451 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3343164.010701 
## iter  10 value 3322614.096277
## iter  20 value 3322585.932428
## final  value 3322584.993845 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3360283.966526 
## iter  10 value 3322724.268317
## iter  20 value 3322585.709251
## final  value 3322585.016027 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3348841.992342 
## iter  10 value 3322594.570801
## iter  20 value 3322586.088518
## final  value 3322584.993839 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3355994.149767 
## iter  10 value 3322818.673707
## iter  20 value 3322586.022381
## final  value 3322583.800698 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3360985.378592 
## iter  10 value 3322596.533523
## final  value 3322583.755098 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3354112.719633 
## iter  10 value 3322607.255229
## final  value 3322583.857860 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3367454.635042 
## iter  10 value 3322594.335580
## iter  20 value 3322587.443773
## iter  30 value 3322584.499428
## final  value 3322584.397657 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3346903.473975 
## iter  10 value 3322731.796685
## iter  20 value 3322584.113076
## final  value 3322583.788969 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3354050.956614 
## iter  10 value 3322649.640179
## iter  20 value 3322581.796250
## final  value 3322581.059277 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3354254.502287 
## iter  10 value 3322669.471541
## iter  20 value 3322582.024346
## final  value 3322581.068946 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3357114.271803 
## iter  10 value 3322630.773834
## iter  20 value 3322581.578724
## final  value 3322581.044326 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3347853.940275 
## iter  10 value 3322651.521589
## iter  20 value 3322581.818172
## final  value 3322581.060999 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3339812.220199 
## iter  10 value 3322638.798029
## iter  20 value 3322581.670915
## final  value 3322581.050356 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3353530.468451 
## iter  10 value 3322668.476089
## iter  20 value 3322582.023511
## final  value 3322581.059453 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3350531.362892 
## iter  10 value 3322652.707406
## iter  20 value 3322581.845319
## final  value 3322581.075548 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3335698.400011 
## iter  10 value 3322617.475870
## iter  20 value 3322581.437968
## final  value 3322581.062702 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3341933.459169 
## iter  10 value 3322645.025791
## iter  20 value 3322581.755250
## final  value 3322581.067922 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3341660.036135 
## iter  10 value 3322666.144893
## iter  20 value 3322581.998753
## final  value 3322581.074290 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3335053.085308 
## iter  10 value 3322659.111681
## iter  20 value 3322581.926109
## final  value 3322581.082759 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3354709.682294 
## iter  10 value 3322723.180565
## iter  20 value 3322582.668582
## final  value 3322581.140396 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3337666.346257 
## final  value 3322581.037881 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3351370.644766 
## iter  10 value 3322683.323129
## iter  20 value 3322582.206956
## final  value 3322581.102152 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3364005.444278 
## iter  10 value 3322664.102140
## iter  20 value 3322581.988959
## final  value 3322581.184018 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 2983937.641968 
## final  value 2951080.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 2972991.195044 
## final  value 2951080.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 2988041.040424 
## final  value 2951080.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 2976562.401469 
## final  value 2951080.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 2982931.972008 
## final  value 2951080.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 2976538.251978 
## final  value 2951080.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 2969302.113013 
## final  value 2951080.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 2988430.309895 
## final  value 2951080.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 2971224.835532 
## final  value 2951080.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 2983779.741149 
## final  value 2951080.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 2992278.610882 
## final  value 2951080.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 2993304.525469 
## final  value 2951080.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 2970034.023931 
## final  value 2951080.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 2977917.521659 
## final  value 2951080.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 2992403.258087 
## final  value 2951080.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 2982335.139219 
## iter  10 value 2951205.661241
## final  value 2951086.955861 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 2971093.078487 
## iter  10 value 2951167.085918
## final  value 2951086.977556 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 2975860.072646 
## iter  10 value 2951093.281031
## iter  20 value 2951087.020848
## iter  20 value 2951086.995853
## iter  20 value 2951086.977222
## final  value 2951086.977222 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 2975683.499517 
## iter  10 value 2951108.480866
## iter  20 value 2951087.005527
## iter  20 value 2951086.985764
## iter  20 value 2951086.970414
## final  value 2951086.970414 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 2986412.203610 
## iter  10 value 2951087.068249
## final  value 2951086.951956 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 2985918.728454 
## iter  10 value 2951093.850423
## final  value 2951084.962906 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 2979757.371043 
## iter  10 value 2951156.537056
## iter  20 value 2951087.455641
## final  value 2951086.968359 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 2970115.049700 
## iter  10 value 2951208.295121
## iter  20 value 2951085.423175
## iter  30 value 2951084.187273
## final  value 2951083.871154 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 2970137.651260 
## iter  10 value 2951291.104661
## final  value 2951085.614420 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 2976488.918157 
## iter  10 value 2951347.043831
## iter  20 value 2951084.091205
## final  value 2951083.880255 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 2989133.207623 
## iter  10 value 2951115.956395
## iter  20 value 2951083.215465
## final  value 2951082.873321 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 2972843.987415 
## iter  10 value 2951110.061258
## iter  20 value 2951083.420315
## final  value 2951082.752198 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 2977360.210997 
## iter  10 value 2951279.347862
## iter  20 value 2951083.258794
## final  value 2951083.155399 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 2963327.532860 
## iter  10 value 2951224.410395
## iter  20 value 2951083.190672
## final  value 2951082.824641 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 2962368.991243 
## iter  10 value 2951134.068013
## iter  20 value 2951083.693250
## final  value 2951083.270198 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 2986160.188435 
## iter  10 value 2951139.232354
## iter  20 value 2951080.687719
## final  value 2951080.051761 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 2985893.136742 
## iter  10 value 2951152.343991
## iter  20 value 2951080.839488
## final  value 2951080.087964 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 2972467.460545 
## iter  10 value 2951110.554305
## iter  20 value 2951080.358417
## final  value 2951080.044004 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 2985495.545499 
## iter  10 value 2951137.707987
## iter  20 value 2951080.670242
## final  value 2951080.050652 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 2982618.371788 
## iter  10 value 2951137.990117
## iter  20 value 2951080.675184
## final  value 2951080.052578 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 2987186.801621 
## iter  10 value 2951115.226696
## iter  20 value 2951080.421769
## final  value 2951080.059345 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 2965900.473070 
## iter  10 value 2951130.920447
## iter  20 value 2951080.601772
## final  value 2951080.055141 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 2969499.300634 
## final  value 2951080.037960 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 2986265.022869 
## iter  10 value 2951119.602874
## iter  20 value 2951080.473371
## final  value 2951080.065917 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 2969196.165831 
## iter  10 value 2951122.513248
## iter  20 value 2951080.507336
## final  value 2951080.050996 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 2988025.254504 
## iter  10 value 2951231.201198
## iter  20 value 2951081.772837
## final  value 2951080.213174 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 2986316.142474 
## final  value 2951082.229368 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 2967909.464767 
## final  value 2951080.036809 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 2975798.295210 
## iter  10 value 2951198.452561
## iter  20 value 2951081.394030
## final  value 2951080.067005 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 2977927.150987 
## iter  10 value 2951114.070995
## iter  20 value 2951080.421364
## final  value 2951080.070938 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3183958.296403 
## final  value 3152652.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3189768.019246 
## final  value 3152652.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3174405.215611 
## final  value 3152652.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3167863.924193 
## final  value 3152652.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3181219.587529 
## final  value 3152652.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3187025.284159 
## final  value 3152652.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3185406.796906 
## final  value 3152652.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3186408.271585 
## final  value 3152652.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3189023.448288 
## final  value 3152652.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3186122.532604 
## final  value 3152652.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3196698.095643 
## final  value 3152652.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3191865.357260 
## final  value 3152652.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3172365.603285 
## final  value 3152652.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3166321.514404 
## final  value 3152652.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3179636.520249 
## final  value 3152652.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3184020.208167 
## iter  10 value 3152740.366871
## iter  20 value 3152659.923113
## final  value 3152658.965584 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3184955.406841 
## iter  10 value 3152674.728985
## final  value 3152658.967448 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3181607.171174 
## iter  10 value 3152820.907834
## iter  20 value 3152660.382017
## final  value 3152658.978935 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3174091.478746 
## iter  10 value 3152667.007247
## iter  20 value 3152659.002991
## iter  20 value 3152658.991694
## iter  20 value 3152658.986198
## final  value 3152658.986198 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3188058.901140 
## iter  10 value 3152665.362141
## final  value 3152659.003159 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3184593.803473 
## iter  10 value 3152666.764846
## iter  20 value 3152656.538823
## final  value 3152655.901750 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3165470.186602 
## iter  10 value 3152693.862627
## iter  20 value 3152656.302283
## final  value 3152655.878122 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3187862.777002 
## iter  10 value 3152778.329123
## iter  20 value 3152656.001632
## final  value 3152655.907787 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3170922.817357 
## iter  10 value 3152876.358059
## iter  20 value 3152657.100876
## iter  30 value 3152656.159191
## iter  30 value 3152656.136981
## iter  30 value 3152656.128205
## final  value 3152656.128205 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3158803.185686 
## iter  10 value 3152696.650043
## iter  20 value 3152656.346866
## final  value 3152655.872640 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3186141.522499 
## iter  10 value 3152843.014545
## iter  20 value 3152700.508972
## iter  30 value 3152657.135962
## iter  40 value 3152654.832140
## final  value 3152654.761339 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3179099.438030 
## iter  10 value 3152664.326529
## iter  20 value 3152655.305537
## iter  20 value 3152655.291360
## final  value 3152654.776797 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3172269.858143 
## iter  10 value 3153024.547745
## iter  20 value 3152656.491476
## final  value 3152654.780689 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3164474.924075 
## iter  10 value 3152932.968974
## iter  20 value 3152656.983004
## iter  30 value 3152655.129262
## final  value 3152654.814458 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3179430.944202 
## iter  10 value 3153105.938186
## iter  20 value 3152655.549385
## final  value 3152654.818670 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3186267.215217 
## iter  10 value 3152684.418411
## iter  20 value 3152652.380013
## final  value 3152652.046417 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3183279.629039 
## iter  10 value 3152720.820758
## iter  20 value 3152652.799739
## final  value 3152652.084975 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3175934.600326 
## iter  10 value 3152684.942278
## iter  20 value 3152652.384761
## final  value 3152652.045762 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3171539.614017 
## iter  10 value 3152707.427861
## iter  20 value 3152652.645429
## final  value 3152652.069651 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3174559.466600 
## iter  10 value 3152688.350734
## iter  20 value 3152652.424636
## final  value 3152652.050563 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3196852.451387 
## iter  10 value 3152715.723699
## iter  20 value 3152652.750304
## final  value 3152652.093343 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3188556.259984 
## iter  10 value 3152806.994888
## iter  20 value 3152653.804534
## final  value 3152652.200685 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3183687.230169 
## iter  10 value 3152724.132603
## iter  20 value 3152652.850631
## final  value 3152652.071798 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3187472.623688 
## iter  10 value 3152715.563537
## iter  20 value 3152652.748789
## final  value 3152652.066416 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3180303.432629 
## iter  10 value 3152688.615009
## iter  20 value 3152652.440867
## final  value 3152652.064180 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3166853.895167 
## iter  10 value 3152697.945997
## iter  20 value 3152652.556951
## final  value 3152652.082784 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3183139.269114 
## iter  10 value 3152779.920147
## iter  20 value 3152653.501475
## final  value 3152652.068354 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3173118.876163 
## iter  10 value 3152713.581474
## iter  20 value 3152652.738039
## final  value 3152652.077108 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3180348.276490 
## iter  10 value 3152748.461167
## iter  20 value 3152653.141295
## final  value 3152652.078293 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3183491.844798 
## iter  10 value 3152821.575238
## iter  20 value 3152653.982507
## final  value 3152652.101381 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3236865.231689 
## final  value 3210489.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3239683.337529 
## final  value 3210489.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3237571.162953 
## final  value 3210489.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3241683.854458 
## final  value 3210489.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3243840.398774 
## final  value 3210489.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3249904.510416 
## final  value 3210489.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3239394.993487 
## final  value 3210489.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3232465.475027 
## final  value 3210489.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3234959.302132 
## final  value 3210489.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3221500.945849 
## final  value 3210489.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3238036.149730 
## final  value 3210489.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3233830.019397 
## final  value 3210489.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3225396.678424 
## final  value 3210489.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3252834.493737 
## final  value 3210489.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3236499.847331 
## final  value 3210489.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3231226.941850 
## iter  10 value 3210548.073887
## final  value 3210495.987300 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3245617.223155 
## iter  10 value 3210500.762314
## final  value 3210495.989830 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3230454.018271 
## iter  10 value 3210516.312005
## iter  20 value 3210496.391246
## final  value 3210495.999653 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3244058.486628 
## iter  10 value 3210500.529682
## final  value 3210496.019667 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3230499.231785 
## iter  10 value 3210500.022131
## final  value 3210495.999811 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3235327.512153 
## iter  10 value 3210637.658641
## final  value 3210492.887574 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3236692.865139 
## iter  10 value 3210503.613316
## final  value 3210494.074581 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3245355.514541 
## iter  10 value 3210503.939222
## iter  20 value 3210494.273309
## final  value 3210493.052988 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3248111.731616 
## iter  10 value 3210501.773697
## final  value 3210493.337362 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3246414.322154 
## iter  10 value 3210493.746679
## final  value 3210492.894176 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3243634.752601 
## iter  10 value 3210500.106820
## iter  20 value 3210492.356249
## final  value 3210492.262975 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3243845.740188 
## iter  10 value 3210727.886293
## iter  20 value 3210492.831673
## final  value 3210491.774491 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3249652.182782 
## iter  10 value 3210696.248760
## iter  20 value 3210495.577448
## final  value 3210491.792256 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3247983.018407 
## iter  10 value 3210646.765685
## iter  20 value 3210492.189706
## final  value 3210491.774327 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3238097.115167 
## iter  10 value 3210503.748876
## iter  20 value 3210491.850170
## iter  20 value 3210491.849681
## final  value 3210491.790583 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3228589.160919 
## iter  10 value 3210541.099614
## iter  20 value 3210489.606473
## final  value 3210489.047109 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3229736.224214 
## iter  10 value 3210517.857456
## iter  20 value 3210489.338487
## final  value 3210489.061618 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3231836.477872 
## iter  10 value 3210523.049008
## iter  20 value 3210489.399044
## final  value 3210489.048667 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3231443.062836 
## iter  10 value 3210551.410954
## iter  20 value 3210489.724007
## final  value 3210489.053916 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3247385.935800 
## iter  10 value 3211256.785916
## iter  20 value 3210497.851969
## iter  30 value 3210489.102056
## final  value 3210489.041781 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3226988.518438 
## iter  10 value 3210538.844257
## iter  20 value 3210489.590780
## final  value 3210489.073087 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3257706.499522 
## iter  10 value 3210544.745485
## iter  20 value 3210489.658326
## final  value 3210489.121811 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3242705.216640 
## iter  10 value 3210560.646223
## iter  20 value 3210489.841924
## final  value 3210489.097734 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3252123.241883 
## iter  10 value 3210536.598407
## iter  20 value 3210489.567024
## final  value 3210489.072681 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3249928.319934 
## iter  10 value 3210569.408863
## iter  20 value 3210489.946825
## final  value 3210489.078965 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3228228.686666 
## final  value 3210489.041126 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3247325.064130 
## iter  10 value 3210612.001573
## iter  20 value 3210490.443471
## final  value 3210489.115338 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3244486.752505 
## iter  10 value 3210617.639517
## iter  20 value 3210490.513142
## final  value 3210489.090357 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3234313.627180 
## iter  10 value 3210589.720602
## iter  20 value 3210490.191603
## final  value 3210489.104130 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3225794.982609 
## iter  10 value 3210583.697194
## iter  20 value 3210490.122124
## final  value 3210489.085971 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 2909135.810521 
## final  value 2889563.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 2924046.886683 
## final  value 2889563.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 2919189.180563 
## final  value 2889563.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 2927699.817846 
## final  value 2889563.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 2910504.264255 
## final  value 2889563.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 2910226.476466 
## final  value 2889563.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 2909691.396113 
## final  value 2889563.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 2906939.895045 
## final  value 2889563.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 2909313.575853 
## final  value 2889563.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 2912172.431765 
## final  value 2889563.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 2902292.753715 
## final  value 2889563.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 2908731.550557 
## final  value 2889563.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 2921137.553785 
## final  value 2889563.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 2915679.238829 
## final  value 2889563.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 2921641.724144 
## final  value 2889563.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 2922168.244371 
## iter  10 value 2889574.367315
## final  value 2889573.347810 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 2917175.698520 
## iter  10 value 2889572.061430
## final  value 2889569.967701 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 2924873.410110 
## iter  10 value 2890700.033394
## iter  20 value 2889578.024950
## final  value 2889569.980109 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 2913088.946698 
## iter  10 value 2889767.866809
## final  value 2889575.424451 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 2908465.057570 
## iter  10 value 2889767.172735
## final  value 2889569.950204 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 2932702.590289 
## iter  10 value 2889573.555773
## iter  20 value 2889566.933777
## iter  20 value 2889566.930423
## iter  20 value 2889566.925416
## final  value 2889566.925416 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 2919898.184009 
## iter  10 value 2889600.769462
## iter  20 value 2889567.070856
## final  value 2889566.868178 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 2923593.515042 
## iter  10 value 2889639.095797
## iter  20 value 2889567.674373
## final  value 2889566.888910 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 2917922.554080 
## iter  10 value 2889569.363558
## final  value 2889566.891277 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 2915995.086187 
## iter  10 value 2889593.817887
## iter  20 value 2889567.241114
## final  value 2889566.886572 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 2911839.158722 
## iter  10 value 2889687.375091
## iter  20 value 2889567.174456
## final  value 2889566.237588 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 2926094.398777 
## iter  10 value 2889588.794031
## iter  20 value 2889567.305635
## iter  30 value 2889565.774453
## iter  30 value 2889565.773714
## iter  30 value 2889565.764826
## final  value 2889565.764826 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 2912655.977420 
## iter  10 value 2889729.049123
## iter  20 value 2889566.175194
## final  value 2889566.040785 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 2902490.490814 
## iter  10 value 2890245.239886
## iter  20 value 2889585.584261
## iter  30 value 2889565.966445
## final  value 2889565.764332 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 2913443.325227 
## iter  10 value 2889905.763989
## iter  20 value 2889565.840658
## iter  20 value 2889565.828083
## final  value 2889565.743922 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 2916858.126049 
## iter  10 value 2889596.426511
## iter  20 value 2889563.392374
## final  value 2889563.048408 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 2916496.646861 
## iter  10 value 2889596.396787
## iter  20 value 2889563.390470
## final  value 2889563.046798 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 2923058.989788 
## iter  10 value 2889630.125533
## iter  20 value 2889563.779310
## final  value 2889563.054441 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 2927313.036011 
## iter  10 value 2889611.667700
## iter  20 value 2889563.565854
## final  value 2889563.060293 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 2917243.644155 
## iter  10 value 2889596.428122
## iter  20 value 2889563.389166
## final  value 2889563.045157 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 2914374.989599 
## iter  10 value 2889673.196445
## iter  20 value 2889564.291433
## final  value 2889563.056860 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 2910016.868796 
## final  value 2889563.152412 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 2923051.362043 
## iter  10 value 2889619.191221
## iter  20 value 2889563.664633
## final  value 2889563.062919 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 2901844.125519 
## iter  10 value 2889595.058158
## iter  20 value 2889563.384745
## final  value 2889563.072382 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 2917832.566311 
## iter  10 value 2889737.102844
## iter  20 value 2889565.022431
## final  value 2889563.188843 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 2904999.641787 
## iter  10 value 2889626.328520
## iter  20 value 2889563.758254
## final  value 2889563.078502 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 2924361.994434 
## final  value 2889565.908759 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 2929703.290995 
## final  value 2889564.450251 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 2912313.370796 
## final  value 2889563.038550 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 2920120.197519 
## iter  10 value 2889723.025922
## iter  20 value 2889564.874811
## final  value 2889563.087198 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3171992.784106 
## final  value 3151930.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3177897.280204 
## final  value 3151930.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3191365.014570 
## final  value 3151930.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3178936.160752 
## final  value 3151930.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3177395.291885 
## final  value 3151930.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3180654.077619 
## final  value 3151930.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3181895.830828 
## final  value 3151930.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3171564.847014 
## final  value 3151930.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3170091.865897 
## final  value 3151930.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3169571.594557 
## final  value 3151930.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3181776.645041 
## final  value 3151930.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3170140.194304 
## final  value 3151930.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3186099.842891 
## final  value 3151930.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3178743.096037 
## final  value 3151930.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3170911.924014 
## final  value 3151930.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3172766.301898 
## iter  10 value 3151940.314718
## final  value 3151936.974259 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3176217.168445 
## iter  10 value 3151937.206391
## final  value 3151936.997269 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3177967.947673 
## iter  10 value 3151938.477525
## final  value 3151936.993748 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3180394.295430 
## iter  10 value 3151944.431079
## final  value 3151937.008161 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3192578.000757 
## iter  10 value 3151963.638808
## final  value 3151936.971543 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3187783.871876 
## iter  10 value 3152130.948634
## iter  20 value 3151938.792800
## final  value 3151933.877149 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3164986.451752 
## iter  10 value 3152065.328135
## final  value 3151934.762174 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3173735.300911 
## iter  10 value 3151947.139347
## iter  20 value 3151934.117417
## final  value 3151933.902378 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3185910.665131 
## iter  10 value 3151955.334992
## iter  20 value 3151934.389126
## final  value 3151933.996954 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3189619.929744 
## iter  10 value 3152026.076230
## iter  20 value 3151934.857147
## iter  20 value 3151934.856358
## iter  20 value 3151934.826781
## final  value 3151934.826781 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3173745.433874 
## iter  10 value 3151940.493539
## iter  20 value 3151933.303075
## final  value 3151932.773634 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3176586.635787 
## iter  10 value 3151963.678903
## final  value 3151935.027150 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3171856.950634 
## iter  10 value 3152143.103743
## iter  20 value 3151933.239530
## iter  20 value 3151933.221754
## final  value 3151932.766068 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3177336.936881 
## iter  10 value 3152271.056775
## iter  20 value 3151933.740330
## final  value 3151932.772643 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3180114.419999 
## iter  10 value 3151941.115871
## final  value 3151934.998353 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3187720.014542 
## iter  10 value 3152695.831117
## iter  20 value 3151938.829432
## iter  30 value 3151930.101796
## final  value 3151930.041675 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3178088.899963 
## iter  10 value 3151990.395368
## iter  20 value 3151930.703162
## final  value 3151930.054732 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3173280.656934 
## iter  10 value 3151989.181802
## iter  20 value 3151930.686732
## final  value 3151930.071940 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3181503.506073 
## iter  10 value 3152093.992564
## iter  20 value 3151931.895579
## final  value 3151930.124605 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3185511.490464 
## iter  10 value 3151994.870683
## iter  20 value 3151930.752600
## final  value 3151930.056100 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 3190135.791792 
## iter  10 value 3152118.953708
## iter  20 value 3151932.197790
## final  value 3151930.155366 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 3168463.569019 
## iter  10 value 3152006.941774
## iter  20 value 3151930.905605
## final  value 3151930.057669 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 3173336.111211 
## final  value 3151930.043880 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 3181105.235450 
## iter  10 value 3152010.091104
## iter  20 value 3151930.940299
## final  value 3151930.110411 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 3191223.715251 
## iter  10 value 3152006.243986
## iter  20 value 3151930.895068
## final  value 3151930.076081 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 3190871.119731 
## iter  10 value 3151988.023785
## iter  20 value 3151930.698230
## final  value 3151930.075446 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 3172289.791721 
## iter  10 value 3152049.929368
## iter  20 value 3151931.409832
## final  value 3151930.073455 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 3166414.363384 
## iter  10 value 3151977.751945
## iter  20 value 3151930.580997
## final  value 3151930.115780 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 3170436.964477 
## iter  10 value 3152055.288736
## iter  20 value 3151931.466437
## final  value 3151930.080654 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 3178751.220177 
## iter  10 value 3152072.189817
## iter  20 value 3151931.670571
## final  value 3151930.097900 
## converged
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 3526980.085461 
## final  value 3498931.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 3534504.220153 
## final  value 3498931.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 3535764.641567 
## final  value 3498931.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 3528075.635748 
## final  value 3498931.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 3538347.771156 
## final  value 3498931.000000 
## converged
## [1] "xgbTree"
## [1] "xgbLinear"
colnames(performetrics)[1]<- "Method"
colnames(performetrics)[2]<- "MAE"
colnames(performetrics)[3]<- "RMSE"

performetrics
##      Method      MAE     RMSE
## 1        rf 23.32156 35.88731
## 2       mlp 30.71033 53.21878
## 3     rpart 23.81891 38.39126
## 4 svmLinear 23.32621 49.66616
## 5 svmRadial 23.40527 48.87840
## 6     parRF 23.44037 35.89202
## 7    avNNet 33.19010 59.98460
## 8   xgbTree 23.23430 35.69489
## 9 xgbLinear 24.07284 38.00687
rm(i, control, methods, model_sj.cv, performetrics)

CV for IQ

library(caret)

set.seed(136)

methods <- c("rf", "mlp", "rpart", "svmLinear", "svmRadial",  "parRF", "avNNet", "xgbTree", "xgbLinear")
performetrics <- data.frame()
#trainControl
control <- trainControl(method="repeatedcv", number=10, repeats=3)

for (i in 1:length(methods)){
  #Train the model
  print(methods[i])
  model_iq.cv <- train(total_cases~.,
                       data=iq_train_labels.lastna[3:23],
                       method=methods[i],
                       trControl=control)
  # summarize results
  #print(methods[i])
  #model_sj.cv$results["MAE"]
  #model_sj.cv$results["RMSE"]
  performetrics[i,1] <- methods[i]
  performetrics[i,2] <- min(model_iq.cv$results["MAE"])
  performetrics[i,3] <- min(model_iq.cv$results["RMSE"])  

}
## [1] "rf"
## [1] "mlp"
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## [1] "rpart"
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## [1] "svmLinear"
## [1] "svmRadial"
## [1] "parRF"
## [1] "avNNet"
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 82346.320226 
## final  value 78444.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 81863.149722 
## final  value 78444.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 82221.827685 
## final  value 78444.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81957.575216 
## final  value 78444.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 82463.135553 
## final  value 78444.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81152.245620 
## final  value 78444.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 82716.141152 
## final  value 78444.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79665.165511 
## final  value 78444.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 81387.970346 
## final  value 78444.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80473.293783 
## final  value 78444.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 82481.059514 
## final  value 78444.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 81199.735696 
## final  value 78444.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 79836.246984 
## final  value 78444.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 80932.713749 
## final  value 78444.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 81373.177176 
## final  value 78444.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 82625.627158 
## iter  10 value 78449.292535
## final  value 78448.809069 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 83287.995768 
## iter  10 value 78452.286166
## final  value 78448.810701 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 81438.261774 
## iter  10 value 78449.572554
## final  value 78448.809476 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 82166.629133 
## iter  10 value 78448.928248
## final  value 78448.809333 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 82596.789361 
## iter  10 value 78453.287832
## iter  20 value 78449.019190
## iter  30 value 78448.810558
## final  value 78448.809080 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 82090.551466 
## iter  10 value 78452.243440
## iter  20 value 78447.365496
## iter  30 value 78446.851355
## iter  40 value 78446.745938
## final  value 78446.723761 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 82298.389244 
## iter  10 value 78453.467817
## iter  20 value 78447.391501
## iter  30 value 78446.747081
## final  value 78446.732432 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 80348.247920 
## iter  10 value 78449.126481
## iter  20 value 78446.727059
## final  value 78446.724217 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 80050.157121 
## iter  10 value 78491.036348
## iter  20 value 78446.743504
## final  value 78446.723783 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 82294.228173 
## iter  10 value 78467.155798
## iter  20 value 78447.204783
## iter  30 value 78446.726947
## final  value 78446.723837 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 81058.336787 
## iter  10 value 78450.517614
## iter  20 value 78446.802875
## iter  30 value 78446.521614
## iter  40 value 78445.996586
## iter  50 value 78445.961750
## final  value 78445.947763 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 81239.237418 
## iter  10 value 78502.581682
## iter  20 value 78447.030863
## iter  30 value 78446.818115
## iter  40 value 78446.250111
## iter  50 value 78445.980336
## final  value 78445.955498 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 83868.337224 
## iter  10 value 78460.736267
## iter  20 value 78447.482094
## iter  30 value 78446.527072
## iter  40 value 78446.314795
## iter  50 value 78446.044889
## iter  60 value 78445.948907
## iter  60 value 78445.948348
## iter  60 value 78445.948115
## final  value 78445.948115 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 81379.636345 
## iter  10 value 78448.878882
## iter  20 value 78446.034300
## final  value 78445.948430 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 81682.610524 
## iter  10 value 78447.206996
## iter  20 value 78446.336018
## iter  30 value 78446.267223
## iter  30 value 78446.266585
## iter  30 value 78446.266414
## final  value 78446.266414 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 82133.510771 
## iter  10 value 78456.075744
## iter  20 value 78444.139224
## final  value 78444.022976 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 82419.647968 
## iter  10 value 78469.196568
## iter  20 value 78444.290497
## final  value 78444.048958 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 82550.675576 
## iter  10 value 78463.623385
## iter  20 value 78444.226242
## final  value 78444.039971 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81608.647377 
## iter  10 value 78468.203708
## iter  20 value 78444.279050
## final  value 78444.043848 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80902.260163 
## iter  10 value 78454.665094
## iter  20 value 78444.122960
## final  value 78444.024166 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 83216.603377 
## iter  10 value 78522.612422
## iter  20 value 78444.906340
## final  value 78444.175010 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 82306.574879 
## iter  10 value 78471.184935
## iter  20 value 78444.313421
## final  value 78444.036258 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 82408.741723 
## iter  10 value 78492.583165
## iter  20 value 78444.560126
## final  value 78444.051405 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 81867.944895 
## iter  10 value 78464.102925
## iter  20 value 78444.231771
## final  value 78444.024480 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 82920.184132 
## iter  10 value 78478.936984
## iter  20 value 78444.402796
## final  value 78444.022045 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 84284.593755 
## iter  10 value 78473.463512
## iter  20 value 78444.339691
## final  value 78444.065277 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 81996.441500 
## iter  10 value 78485.567141
## iter  20 value 78444.479237
## iter  30 value 78444.010617
## iter  30 value 78444.010613
## iter  30 value 78444.010609
## final  value 78444.010609 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 80665.811331 
## iter  10 value 78446.941083
## final  value 78444.102862 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 81444.007450 
## iter  10 value 78507.770189
## iter  20 value 78444.735220
## iter  30 value 78444.017108
## iter  30 value 78444.017101
## iter  30 value 78444.017094
## final  value 78444.017094 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 80843.973308 
## iter  10 value 78483.250618
## iter  20 value 78444.452529
## iter  30 value 78444.009701
## iter  30 value 78444.009697
## iter  30 value 78444.009693
## final  value 78444.009693 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 72037.135886 
## final  value 68772.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 72122.663940 
## final  value 68772.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 72382.035297 
## final  value 68772.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 71698.166439 
## final  value 68772.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 72475.370175 
## final  value 68772.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 72375.094688 
## final  value 68772.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 71883.796296 
## final  value 68772.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 71660.864901 
## final  value 68772.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 73910.126941 
## final  value 68772.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 70573.554722 
## final  value 68772.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 71993.916064 
## final  value 68772.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 69998.429074 
## final  value 68772.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 72222.614207 
## final  value 68772.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 71231.418575 
## final  value 68772.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 71463.747692 
## final  value 68772.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 71763.415650 
## iter  10 value 68780.537100
## iter  20 value 68777.174990
## iter  30 value 68776.777335
## final  value 68776.776103 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 71517.209805 
## iter  10 value 68785.222548
## final  value 68780.372398 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 71939.110289 
## iter  10 value 68780.380176
## final  value 68780.370656 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 70688.606255 
## iter  10 value 68777.641369
## iter  20 value 68776.776779
## iter  20 value 68776.776440
## iter  20 value 68776.776372
## final  value 68776.776372 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 71965.401258 
## iter  10 value 68777.422988
## final  value 68776.777209 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 72037.957468 
## iter  10 value 68798.095256
## iter  20 value 68776.375841
## iter  30 value 68775.017674
## iter  40 value 68774.712456
## final  value 68774.706685 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 72204.841302 
## iter  10 value 68785.772025
## iter  20 value 68774.844972
## iter  30 value 68774.721128
## final  value 68774.706113 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 72523.047459 
## iter  10 value 68776.341521
## final  value 68775.430278 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 71646.857708 
## iter  10 value 68777.975934
## iter  20 value 68775.982691
## iter  30 value 68774.866725
## final  value 68774.707388 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 72725.379229 
## iter  10 value 68779.578850
## iter  20 value 68775.108905
## iter  30 value 68774.719241
## final  value 68774.706748 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 71079.456255 
## iter  10 value 68808.018080
## iter  20 value 68774.235753
## iter  30 value 68773.941418
## final  value 68773.935567 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 70675.991251 
## iter  10 value 68805.067905
## iter  20 value 68775.298343
## iter  30 value 68774.329462
## iter  40 value 68774.252934
## final  value 68774.250886 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 71613.235562 
## iter  10 value 68840.405587
## iter  20 value 68778.450692
## iter  30 value 68775.150389
## iter  40 value 68774.019766
## final  value 68773.935811 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 73142.600093 
## iter  10 value 68810.690658
## iter  20 value 68774.708280
## iter  30 value 68774.026786
## iter  40 value 68773.941175
## final  value 68773.936182 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 73900.694164 
## iter  10 value 68867.633845
## iter  20 value 68842.189917
## iter  30 value 68811.115219
## iter  40 value 68776.678910
## iter  50 value 68775.430102
## iter  60 value 68775.210025
## iter  70 value 68775.016969
## iter  80 value 68774.688758
## iter  90 value 68774.510275
## iter 100 value 68774.277094
## final  value 68774.277094 
## stopped after 100 iterations
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 72525.626834 
## iter  10 value 68784.064571
## iter  20 value 68772.139095
## final  value 68772.014794 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 71105.928587 
## iter  10 value 68791.609410
## iter  20 value 68772.226081
## final  value 68772.011587 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 72432.821482 
## iter  10 value 68784.152483
## iter  20 value 68772.140109
## final  value 68772.026084 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 70770.744618 
## iter  10 value 68807.502264
## iter  20 value 68772.409313
## final  value 68772.037784 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 72430.628442 
## iter  10 value 68794.219427
## iter  20 value 68772.256173
## final  value 68772.013488 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 72214.943374 
## iter  10 value 68800.173744
## iter  20 value 68772.324821
## final  value 68772.015032 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 72883.269702 
## iter  10 value 68783.314493
## iter  20 value 68772.130447
## final  value 68772.025664 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 69699.244859 
## iter  10 value 68780.184773
## iter  20 value 68772.094364
## final  value 68772.009529 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 71140.629745 
## iter  10 value 68805.847918
## iter  20 value 68772.390240
## final  value 68772.042634 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 71147.370516 
## iter  10 value 68812.964431
## iter  20 value 68772.472288
## final  value 68772.038194 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 72207.678498 
## iter  10 value 68806.665004
## iter  20 value 68772.399660
## final  value 68772.043072 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 72303.931001 
## iter  10 value 68832.333342
## iter  20 value 68772.695596
## iter  30 value 68772.011548
## final  value 68772.009690 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 73816.035725 
## iter  10 value 68814.121238
## iter  20 value 68772.485625
## final  value 68772.030513 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 73706.378428 
## iter  10 value 68814.402613
## iter  20 value 68772.488869
## final  value 68772.057019 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 71966.824405 
## iter  10 value 68803.852418
## iter  20 value 68772.367233
## final  value 68772.010180 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 83322.231212 
## final  value 79164.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 83355.361681 
## final  value 79164.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 82049.826359 
## final  value 79164.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81738.635537 
## final  value 79164.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 81511.515613 
## final  value 79164.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 82126.452050 
## final  value 79164.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 82738.196822 
## final  value 79164.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 82324.077301 
## final  value 79164.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 82962.541201 
## final  value 79164.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 81612.159837 
## final  value 79164.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 83602.268107 
## final  value 79164.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 82435.665174 
## final  value 79164.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 83227.416256 
## final  value 79164.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 82598.965360 
## final  value 79164.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 83177.414669 
## final  value 79164.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80779.995306 
## iter  10 value 79170.395421
## iter  20 value 79168.824965
## final  value 79168.820785 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 82129.751782 
## iter  10 value 79183.779273
## iter  20 value 79169.014366
## iter  30 value 79168.823947
## final  value 79168.819997 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 81797.451150 
## iter  10 value 79173.409069
## final  value 79168.819836 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81740.935800 
## iter  10 value 79254.398000
## iter  20 value 79169.817253
## iter  30 value 79168.820857
## iter  30 value 79168.820489
## iter  30 value 79168.820287
## final  value 79168.820287 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 82256.097620 
## iter  10 value 79169.058215
## final  value 79168.819911 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81424.465583 
## iter  10 value 79236.679729
## iter  20 value 79168.531848
## iter  30 value 79166.769755
## final  value 79166.729841 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 82167.338130 
## iter  10 value 79190.079723
## iter  20 value 79166.864709
## iter  30 value 79166.734853
## final  value 79166.729441 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 81394.018592 
## iter  10 value 79170.194005
## final  value 79166.740671 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 80630.276627 
## iter  10 value 79168.200173
## iter  20 value 79166.759999
## iter  30 value 79166.730168
## iter  30 value 79166.730077
## iter  30 value 79166.730077
## final  value 79166.730077 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 82268.248295 
## iter  10 value 79168.851820
## iter  20 value 79167.184373
## iter  30 value 79166.731692
## iter  30 value 79166.731541
## final  value 79166.729590 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 83318.866400 
## iter  10 value 79172.006720
## iter  20 value 79167.667061
## iter  30 value 79166.041130
## iter  40 value 79165.960681
## final  value 79165.952869 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 83365.642149 
## iter  10 value 79170.415690
## iter  20 value 79166.528538
## iter  30 value 79166.208321
## iter  40 value 79165.954816
## final  value 79165.952340 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 80121.674988 
## iter  10 value 79199.970661
## iter  20 value 79167.264173
## iter  30 value 79166.536397
## iter  40 value 79165.970463
## final  value 79165.952262 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 82092.642041 
## iter  10 value 79169.692820
## iter  20 value 79166.216852
## iter  30 value 79165.966435
## final  value 79165.951841 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 83332.403892 
## iter  10 value 79168.799612
## iter  20 value 79166.081786
## iter  30 value 79165.953970
## final  value 79165.952089 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 82389.984684 
## iter  10 value 79196.369522
## iter  20 value 79164.373195
## final  value 79164.027344 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 82720.491227 
## iter  10 value 79176.543500
## iter  20 value 79164.144617
## final  value 79164.023866 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80731.856268 
## iter  10 value 79176.794808
## iter  20 value 79164.147514
## final  value 79164.011948 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81782.785167 
## iter  10 value 79185.684485
## iter  20 value 79164.250005
## final  value 79164.013085 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 83011.922060 
## iter  10 value 79184.087740
## iter  20 value 79164.231596
## final  value 79164.038716 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 82166.341985 
## iter  10 value 79197.414222
## iter  20 value 79164.385240
## iter  30 value 79164.018633
## final  value 79164.014853 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 82113.323565 
## iter  10 value 79185.818752
## iter  20 value 79164.251553
## final  value 79164.013517 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 82972.834622 
## iter  10 value 79210.559604
## iter  20 value 79164.536796
## final  value 79164.080122 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 82023.919178 
## iter  10 value 79204.657818
## iter  20 value 79164.468753
## final  value 79164.072426 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 82089.073563 
## iter  10 value 79200.979939
## iter  20 value 79164.426350
## final  value 79164.022571 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 82286.426092 
## iter  10 value 79207.863918
## iter  20 value 79164.505717
## final  value 79164.022463 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 82948.968730 
## iter  10 value 79205.553746
## iter  20 value 79164.479082
## iter  30 value 79164.018380
## iter  30 value 79164.017821
## final  value 79164.016050 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 81022.016560 
## iter  10 value 79199.407054
## iter  20 value 79164.408216
## final  value 79164.016277 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 82680.791326 
## iter  10 value 79210.431938
## iter  20 value 79164.535324
## final  value 79164.013660 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 82334.184466 
## iter  10 value 79221.441739
## iter  20 value 79164.662258
## iter  30 value 79164.015706
## iter  30 value 79164.015632
## iter  30 value 79164.015559
## final  value 79164.015559 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 75366.699077 
## final  value 73238.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 74526.896302 
## final  value 73238.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 75870.178925 
## final  value 73238.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 76967.682283 
## final  value 73238.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 75301.700186 
## final  value 73238.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 75963.300723 
## final  value 73238.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 76812.282872 
## final  value 73238.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 77339.424834 
## final  value 73238.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 76338.849576 
## final  value 73238.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 76345.368579 
## final  value 73238.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 76444.579669 
## final  value 73238.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 75833.722238 
## final  value 73238.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 75919.238064 
## final  value 73238.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 75132.279208 
## final  value 73238.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 75936.217549 
## final  value 73238.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 77024.419152 
## iter  10 value 73384.442156
## iter  20 value 73247.141163
## iter  30 value 73243.795387
## final  value 73242.786143 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 76918.347935 
## iter  10 value 73250.225223
## iter  20 value 73242.790656
## final  value 73242.786274 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 76827.593158 
## iter  10 value 73246.650838
## iter  20 value 73243.280667
## iter  30 value 73242.901909
## final  value 73242.786126 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 75367.165899 
## iter  10 value 73245.026567
## iter  20 value 73242.805903
## final  value 73242.786025 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 77087.654099 
## iter  10 value 73243.589841
## final  value 73242.786340 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 77407.607831 
## iter  10 value 73272.865453
## iter  20 value 73247.058406
## iter  30 value 73240.927121
## final  value 73240.711262 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 76794.916256 
## iter  10 value 73266.119212
## iter  20 value 73241.627604
## iter  30 value 73240.821320
## iter  40 value 73240.715389
## final  value 73240.711315 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 75409.597880 
## iter  10 value 73246.087526
## iter  20 value 73240.754960
## final  value 73240.714450 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 78481.525902 
## iter  10 value 73243.687595
## iter  20 value 73240.751604
## final  value 73240.711894 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 75646.501654 
## iter  10 value 73245.312821
## iter  20 value 73240.808434
## iter  30 value 73240.716402
## final  value 73240.711279 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 75844.288379 
## iter  10 value 73270.509477
## iter  20 value 73241.996221
## iter  30 value 73241.140901
## iter  40 value 73240.979502
## iter  50 value 73239.980980
## iter  60 value 73239.951901
## final  value 73239.939688 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 75239.583617 
## iter  10 value 73277.891905
## iter  20 value 73241.235665
## iter  30 value 73240.085781
## final  value 73239.941274 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 77377.052694 
## iter  10 value 73276.321608
## iter  20 value 73240.086183
## iter  30 value 73239.943256
## final  value 73239.940950 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 75395.442838 
## iter  10 value 73258.479050
## iter  20 value 73240.035085
## iter  30 value 73239.941406
## final  value 73239.939413 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 74425.762068 
## iter  10 value 73242.514752
## iter  20 value 73241.356832
## iter  30 value 73239.949688
## final  value 73239.947019 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 75737.311859 
## iter  10 value 73258.451506
## iter  20 value 73238.235790
## final  value 73238.013452 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 77403.028805 
## iter  10 value 73264.008139
## iter  20 value 73238.299853
## final  value 73238.057240 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 75989.517010 
## iter  10 value 73260.358278
## iter  20 value 73238.257773
## final  value 73238.013786 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 75647.978711 
## iter  10 value 73248.186953
## iter  20 value 73238.117448
## final  value 73238.023043 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 76216.420608 
## iter  10 value 73251.060746
## iter  20 value 73238.150580
## final  value 73238.018143 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 74745.996312 
## iter  10 value 73268.127334
## iter  20 value 73238.347345
## final  value 73238.008300 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 77791.603772 
## iter  10 value 73262.315253
## iter  20 value 73238.280336
## final  value 73238.017464 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 75794.324658 
## iter  10 value 73263.440782
## iter  20 value 73238.293312
## final  value 73238.014457 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 75399.919433 
## iter  10 value 73273.634495
## iter  20 value 73238.410838
## final  value 73238.021130 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 76494.481354 
## iter  10 value 73288.169888
## iter  20 value 73238.578419
## final  value 73238.018218 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 78375.093717 
## iter  10 value 73257.920908
## iter  20 value 73238.229672
## final  value 73238.011790 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 76082.343710 
## iter  10 value 73297.074167
## iter  20 value 73238.681079
## final  value 73238.017379 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 75657.306035 
## iter  10 value 73270.376275
## iter  20 value 73238.373273
## iter  30 value 73238.008687
## iter  30 value 73238.008683
## iter  30 value 73238.008680
## final  value 73238.008680 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 76096.003576 
## iter  10 value 73265.275504
## iter  20 value 73238.314465
## final  value 73238.041018 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 75410.746090 
## iter  10 value 73267.712859
## iter  20 value 73238.342566
## final  value 73238.008186 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80257.566898 
## final  value 77615.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 81044.349806 
## final  value 77615.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 82236.289079 
## final  value 77615.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 79756.080644 
## final  value 77615.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 81916.179658 
## final  value 77615.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 80826.781179 
## final  value 77615.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 81271.945438 
## final  value 77615.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79228.506487 
## final  value 77615.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 81475.344541 
## final  value 77615.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 81853.779313 
## final  value 77615.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 82642.341441 
## final  value 77615.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79955.013664 
## final  value 77615.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 82222.731517 
## final  value 77615.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 79336.262028 
## final  value 77615.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 81966.351150 
## final  value 77615.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79901.221753 
## iter  10 value 77622.233257
## iter  20 value 77619.817012
## iter  20 value 77619.816883
## final  value 77619.812996 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 81741.315194 
## iter  10 value 77622.920541
## final  value 77619.812782 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 79852.752348 
## iter  10 value 77620.488569
## iter  20 value 77619.862992
## final  value 77619.813548 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80286.113555 
## iter  10 value 77622.606721
## iter  20 value 77619.815473
## final  value 77619.813621 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80155.858216 
## iter  10 value 77625.095431
## iter  20 value 77619.844452
## final  value 77619.812852 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 80008.897756 
## iter  10 value 77637.455055
## iter  20 value 77618.900092
## iter  30 value 77617.737428
## final  value 77617.727533 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80590.633466 
## iter  10 value 77618.984132
## iter  20 value 77617.882605
## iter  30 value 77617.734689
## final  value 77617.727586 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79198.033261 
## iter  10 value 77627.385995
## iter  20 value 77618.988503
## iter  30 value 77617.858508
## final  value 77617.726302 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79763.731228 
## iter  10 value 77619.062227
## iter  20 value 77617.728724
## iter  20 value 77617.727951
## iter  20 value 77617.727806
## final  value 77617.727806 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80273.896510 
## iter  10 value 77619.926902
## iter  20 value 77617.796214
## final  value 77617.725608 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 81764.479704 
## iter  10 value 77656.660366
## iter  20 value 77617.267792
## iter  30 value 77616.964257
## final  value 77616.949045 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 78961.831062 
## iter  10 value 77619.307942
## iter  20 value 77617.260495
## iter  30 value 77616.980905
## final  value 77616.949635 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 82985.577707 
## iter  10 value 77620.786434
## iter  20 value 77617.147255
## final  value 77616.949476 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 80712.229897 
## iter  10 value 77625.951147
## iter  20 value 77620.821755
## iter  30 value 77617.094648
## iter  40 value 77616.957806
## final  value 77616.949622 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 82186.055854 
## iter  10 value 77624.524893
## iter  20 value 77617.078203
## iter  30 value 77616.981343
## final  value 77616.951139 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80426.621576 
## iter  10 value 77626.782919
## iter  20 value 77615.135848
## final  value 77615.022710 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 79775.794785 
## iter  10 value 77630.546895
## iter  20 value 77615.179244
## final  value 77615.025450 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80693.220592 
## iter  10 value 77645.427083
## iter  20 value 77615.350800
## final  value 77615.055132 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80387.217080 
## iter  10 value 77626.687514
## iter  20 value 77615.134748
## final  value 77615.022526 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80156.243723 
## iter  10 value 77642.470569
## iter  20 value 77615.316714
## final  value 77615.017201 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 82552.133986 
## iter  10 value 77640.995070
## iter  20 value 77615.299703
## final  value 77615.022448 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80751.166454 
## iter  10 value 77655.622541
## iter  20 value 77615.468346
## final  value 77615.023661 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79628.300684 
## iter  10 value 77647.764405
## iter  20 value 77615.377748
## final  value 77615.009904 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79800.661102 
## iter  10 value 77630.592093
## iter  20 value 77615.179765
## final  value 77615.018987 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80235.639274 
## iter  10 value 77640.724906
## iter  20 value 77615.296588
## final  value 77615.050612 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 81521.001373 
## iter  10 value 77696.623961
## iter  20 value 77615.941060
## final  value 77615.016710 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79805.072102 
## iter  10 value 77643.558217
## iter  20 value 77615.329254
## final  value 77615.030217 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 80274.619516 
## iter  10 value 77644.739817
## iter  20 value 77615.342877
## final  value 77615.008891 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 81566.181254 
## iter  10 value 77662.176837
## iter  20 value 77615.543912
## iter  30 value 77615.013003
## iter  30 value 77615.012818
## iter  30 value 77615.012815
## final  value 77615.012815 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 81020.177552 
## iter  10 value 77641.075691
## iter  20 value 77615.300632
## final  value 77615.043481 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 75047.360699 
## final  value 72060.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 75323.605639 
## final  value 72060.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 74192.664921 
## final  value 72060.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 74566.596156 
## final  value 72060.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 74596.217678 
## final  value 72060.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 74141.235368 
## final  value 72060.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 73632.921934 
## final  value 72060.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 75784.212547 
## final  value 72060.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 75083.123014 
## final  value 72060.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 73865.548117 
## final  value 72060.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 74432.720539 
## final  value 72060.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 74112.367938 
## final  value 72060.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 76604.132573 
## final  value 72060.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 75444.826944 
## final  value 72060.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 76571.028379 
## final  value 72060.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 74742.069042 
## iter  10 value 72065.216211
## iter  20 value 72064.787877
## iter  20 value 72064.787819
## iter  20 value 72064.787813
## final  value 72064.787813 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 75033.651595 
## iter  10 value 72064.814778
## final  value 72064.789959 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 75817.278002 
## iter  10 value 72064.939809
## final  value 72064.788833 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 75445.161464 
## iter  10 value 72068.488968
## iter  20 value 72064.879520
## final  value 72064.788551 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 76171.900732 
## iter  10 value 72068.517416
## iter  20 value 72064.788243
## iter  20 value 72064.788143
## iter  20 value 72064.787804
## final  value 72064.787804 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 74783.138519 
## iter  10 value 72065.508667
## iter  20 value 72062.831938
## final  value 72062.713314 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 76046.162506 
## iter  10 value 72097.872501
## iter  20 value 72064.992485
## iter  30 value 72063.900706
## iter  40 value 72063.555555
## final  value 72063.436443 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 75735.128812 
## iter  10 value 72065.389099
## iter  20 value 72063.331516
## iter  30 value 72062.717873
## final  value 72062.713209 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 76040.350901 
## iter  10 value 72068.452805
## iter  20 value 72064.053319
## iter  30 value 72062.729649
## final  value 72062.712309 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 74397.991578 
## iter  10 value 72097.720104
## iter  20 value 72069.279922
## iter  30 value 72062.798604
## iter  40 value 72062.719777
## final  value 72062.712634 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 74217.061298 
## iter  10 value 72071.930160
## iter  20 value 72062.764037
## iter  30 value 72062.249858
## iter  40 value 72061.941240
## iter  40 value 72061.940689
## iter  40 value 72061.940218
## final  value 72061.940218 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 73894.345870 
## iter  10 value 72069.862777
## iter  20 value 72064.670321
## iter  30 value 72062.281772
## iter  40 value 72061.981568
## iter  50 value 72061.945386
## final  value 72061.940604 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 74506.180402 
## iter  10 value 72098.178105
## iter  20 value 72062.585266
## iter  30 value 72062.092952
## final  value 72061.943579 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 75140.150006 
## iter  10 value 72096.310087
## iter  20 value 72064.078138
## iter  30 value 72062.519557
## iter  40 value 72062.238380
## iter  50 value 72062.089611
## iter  60 value 72061.987518
## iter  70 value 72061.954642
## final  value 72061.940100 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 75020.149346 
## iter  10 value 72103.170876
## iter  20 value 72062.094949
## iter  30 value 72061.981099
## iter  40 value 72061.952486
## final  value 72061.940373 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 74303.458583 
## iter  10 value 72078.617869
## iter  20 value 72060.214649
## final  value 72060.011358 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 74103.167055 
## iter  10 value 72081.269622
## iter  20 value 72060.245222
## final  value 72060.027891 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 74919.652116 
## iter  10 value 72073.352986
## iter  20 value 72060.153949
## final  value 72060.022231 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 75309.835018 
## iter  10 value 72083.590434
## iter  20 value 72060.271979
## final  value 72060.011919 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 74810.245147 
## iter  10 value 72082.414275
## iter  20 value 72060.258419
## final  value 72060.019614 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 75008.593484 
## iter  10 value 72104.243664
## iter  20 value 72060.510095
## final  value 72060.025039 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 74454.621237 
## iter  10 value 72092.702051
## iter  20 value 72060.377029
## final  value 72060.008952 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 75024.146530 
## iter  10 value 72104.522753
## iter  20 value 72060.513312
## iter  30 value 72060.025721
## final  value 72060.023519 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 74022.465999 
## iter  10 value 72078.874348
## iter  20 value 72060.217606
## final  value 72060.013556 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 72911.683522 
## iter  10 value 72069.520252
## iter  20 value 72060.109761
## final  value 72060.009080 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 74035.307876 
## iter  10 value 72064.423942
## iter  20 value 72060.105513
## iter  20 value 72060.105193
## iter  20 value 72060.104913
## final  value 72060.104913 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 75539.840379 
## iter  10 value 72083.551379
## iter  20 value 72060.271529
## final  value 72060.016011 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 75969.914990 
## iter  10 value 72080.069032
## iter  20 value 72060.231380
## final  value 72060.039610 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 74626.272832 
## iter  10 value 72111.337187
## iter  20 value 72060.591877
## iter  30 value 72060.012079
## iter  30 value 72060.011895
## final  value 72060.008571 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 76157.701143 
## iter  10 value 72096.189107
## iter  20 value 72060.417232
## final  value 72060.061423 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80349.925923 
## final  value 77387.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80484.501206 
## final  value 77387.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 81594.560965 
## final  value 77387.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81018.172847 
## final  value 77387.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80898.520493 
## final  value 77387.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81017.354469 
## final  value 77387.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80715.022740 
## final  value 77387.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79006.511406 
## final  value 77387.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79270.786020 
## final  value 77387.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 78913.073065 
## final  value 77387.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79174.289610 
## final  value 77387.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 78366.198237 
## final  value 77387.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 81233.820593 
## final  value 77387.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 80548.381808 
## final  value 77387.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 78700.053059 
## final  value 77387.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 81141.543394 
## iter  10 value 77393.664309
## iter  20 value 77391.806128
## iter  20 value 77391.805958
## iter  20 value 77391.805958
## final  value 77391.805958 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 79588.595497 
## iter  10 value 77393.225422
## iter  20 value 77391.822506
## final  value 77391.806521 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 81188.945066 
## iter  10 value 77393.933507
## final  value 77391.806326 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80404.134360 
## iter  10 value 77392.287357
## final  value 77391.806152 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80416.531264 
## iter  10 value 77396.200643
## iter  20 value 77391.891833
## final  value 77391.807684 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 79624.152463 
## iter  10 value 77392.682791
## iter  20 value 77389.935171
## iter  30 value 77389.774198
## final  value 77389.723040 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79412.010938 
## iter  10 value 77393.876763
## iter  20 value 77390.544549
## iter  30 value 77389.786427
## final  value 77389.722492 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 82028.682367 
## iter  10 value 77391.722980
## iter  20 value 77390.000726
## final  value 77389.722834 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79738.196006 
## iter  10 value 77410.921714
## iter  20 value 77389.870236
## iter  30 value 77389.734203
## final  value 77389.723708 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80984.125793 
## iter  10 value 77393.089128
## iter  20 value 77389.867834
## iter  30 value 77389.725225
## final  value 77389.722766 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79775.851921 
## iter  10 value 77400.909188
## iter  20 value 77389.531264
## iter  30 value 77389.277518
## iter  40 value 77388.960145
## final  value 77388.947046 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80245.852610 
## iter  10 value 77432.955583
## iter  20 value 77389.109970
## iter  30 value 77388.954835
## final  value 77388.946820 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 82717.058675 
## iter  10 value 77399.056220
## iter  20 value 77389.358901
## iter  30 value 77389.062001
## final  value 77388.946812 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 80674.803341 
## iter  10 value 77390.358077
## iter  20 value 77388.985049
## iter  20 value 77388.984328
## final  value 77388.946624 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 80411.830517 
## iter  10 value 77480.492265
## iter  20 value 77469.779587
## iter  30 value 77399.642790
## iter  40 value 77390.487922
## iter  50 value 77389.718186
## iter  60 value 77389.099520
## final  value 77388.946775 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 81462.527949 
## iter  10 value 77398.071513
## iter  20 value 77387.127646
## final  value 77387.024149 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 81618.015280 
## iter  10 value 77397.569840
## iter  20 value 77387.121862
## final  value 77387.022718 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80511.784709 
## iter  10 value 77393.063354
## iter  20 value 77387.276094
## final  value 77387.021959 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80708.066766 
## iter  10 value 77415.224950
## iter  20 value 77387.325412
## final  value 77387.022236 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80011.007663 
## iter  10 value 77407.992378
## iter  20 value 77387.242026
## final  value 77387.013181 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81145.753779 
## iter  10 value 77422.940099
## iter  20 value 77387.414361
## final  value 77387.063639 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80962.768237 
## iter  10 value 77411.641376
## iter  20 value 77387.284096
## final  value 77387.010376 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79233.132674 
## iter  10 value 77402.732971
## iter  20 value 77387.181389
## final  value 77387.017279 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79474.386767 
## iter  10 value 77389.457471
## final  value 77387.122247 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80816.836268 
## iter  10 value 77423.026726
## iter  20 value 77387.415360
## final  value 77387.010599 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 78674.715916 
## iter  10 value 77409.166850
## iter  20 value 77387.255566
## final  value 77387.010477 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80853.910140 
## iter  10 value 77442.467035
## iter  20 value 77387.639491
## iter  30 value 77387.007373
## iter  30 value 77387.006798
## iter  30 value 77387.006795
## final  value 77387.006795 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 81788.104419 
## iter  10 value 77431.480591
## iter  20 value 77387.512826
## iter  30 value 77387.021781
## iter  30 value 77387.021719
## iter  30 value 77387.021718
## final  value 77387.021718 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 82381.445688 
## iter  10 value 77422.510182
## iter  20 value 77387.409405
## final  value 77387.017862 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 81970.593669 
## iter  10 value 77469.476257
## iter  20 value 77387.950886
## final  value 77387.053548 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79734.948994 
## final  value 76507.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80336.981007 
## final  value 76507.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80850.865449 
## final  value 76507.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80127.223038 
## final  value 76507.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80044.565141 
## final  value 76507.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 77609.797841 
## final  value 76507.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 81171.129248 
## final  value 76507.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79814.185769 
## final  value 76507.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 78090.656446 
## final  value 76507.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 79796.081501 
## final  value 76507.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79821.968091 
## final  value 76507.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 81941.915670 
## final  value 76507.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 79121.878453 
## final  value 76507.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 81184.807566 
## final  value 76507.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 80379.947168 
## final  value 76507.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79623.727548 
## iter  10 value 76512.958621
## final  value 76511.806791 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 79304.071796 
## iter  10 value 76515.468843
## iter  20 value 76511.806812
## iter  20 value 76511.806679
## iter  20 value 76511.806638
## final  value 76511.806638 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 79625.775155 
## iter  10 value 76513.441083
## iter  20 value 76511.978014
## final  value 76511.825388 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 79338.944542 
## iter  10 value 76513.940747
## iter  20 value 76512.201610
## iter  30 value 76511.809231
## final  value 76511.806683 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 79616.447308 
## iter  10 value 76514.456019
## iter  20 value 76511.818685
## final  value 76511.806674 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81059.274821 
## iter  10 value 76515.312517
## iter  20 value 76510.915266
## iter  30 value 76510.079495
## iter  40 value 76509.907471
## iter  50 value 76509.819220
## iter  60 value 76509.725453
## final  value 76509.723263 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80229.981391 
## iter  10 value 76513.624390
## iter  20 value 76510.683102
## iter  30 value 76509.735198
## final  value 76509.722610 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79058.026753 
## iter  10 value 76514.913881
## iter  20 value 76509.743374
## final  value 76509.722576 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 78736.385244 
## iter  10 value 76515.974362
## iter  20 value 76510.124424
## iter  30 value 76509.795673
## iter  40 value 76509.723275
## iter  40 value 76509.723100
## iter  40 value 76509.723100
## final  value 76509.723100 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80315.738603 
## iter  10 value 76528.854382
## iter  20 value 76509.996152
## iter  30 value 76509.731730
## final  value 76509.722245 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80510.980099 
## iter  10 value 76549.543501
## iter  20 value 76509.768867
## iter  30 value 76509.187353
## iter  40 value 76509.116110
## iter  50 value 76509.016705
## iter  60 value 76508.949744
## final  value 76508.947216 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79118.080484 
## iter  10 value 76510.544152
## iter  20 value 76509.081667
## final  value 76508.946918 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 82146.359903 
## iter  10 value 76518.567099
## iter  20 value 76509.904527
## iter  30 value 76508.998892
## final  value 76508.946777 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 79200.545012 
## iter  10 value 76510.702624
## iter  20 value 76509.288002
## iter  30 value 76508.949597
## final  value 76508.946712 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 79685.803754 
## iter  10 value 76512.016989
## iter  20 value 76509.292733
## iter  30 value 76508.993500
## final  value 76508.947548 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79952.115521 
## iter  10 value 76531.483875
## iter  20 value 76507.282280
## iter  30 value 76507.013397
## final  value 76507.011681 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80050.744100 
## iter  10 value 76530.174059
## iter  20 value 76507.267179
## final  value 76507.011559 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 79776.866559 
## iter  10 value 76519.347541
## iter  20 value 76507.142357
## final  value 76507.023494 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 78282.539161 
## iter  10 value 76527.676438
## iter  20 value 76507.238383
## final  value 76507.030136 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 79626.266277 
## iter  10 value 76519.184229
## iter  20 value 76507.140475
## final  value 76507.021365 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 79004.998616 
## iter  10 value 76541.076748
## iter  20 value 76507.392878
## final  value 76507.018276 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80566.841061 
## iter  10 value 76570.702458
## iter  20 value 76507.734439
## final  value 76507.111876 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 78358.644547 
## iter  10 value 76530.468973
## iter  20 value 76507.270579
## final  value 76507.016856 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 81194.697961 
## iter  10 value 76536.361990
## iter  20 value 76507.338521
## final  value 76507.021089 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 77509.659475 
## iter  10 value 76517.865750
## iter  20 value 76507.125274
## final  value 76507.007931 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 78763.646548 
## iter  10 value 76556.027183
## iter  20 value 76507.565245
## final  value 76507.022537 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79145.854378 
## iter  10 value 76510.052311
## iter  20 value 76507.091891
## final  value 76507.076648 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 79268.671551 
## iter  10 value 76563.849706
## iter  20 value 76507.655432
## final  value 76507.028641 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 80505.251321 
## iter  10 value 76573.942516
## iter  20 value 76507.771795
## iter  30 value 76507.008898
## iter  30 value 76507.008874
## iter  30 value 76507.008870
## final  value 76507.008870 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 79285.413008 
## iter  10 value 76565.841634
## iter  20 value 76507.678398
## final  value 76507.018783 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80062.985148 
## final  value 75954.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 77896.751395 
## final  value 75954.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80182.214745 
## final  value 75954.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 79642.080742 
## final  value 75954.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 79927.962224 
## final  value 75954.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 78371.118760 
## final  value 75954.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79212.493780 
## final  value 75954.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 78593.254249 
## final  value 75954.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 78587.887547 
## final  value 75954.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 79351.568546 
## final  value 75954.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79238.092658 
## final  value 75954.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80401.978202 
## final  value 75954.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 78628.633044 
## final  value 75954.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 78686.048694 
## final  value 75954.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 77784.373043 
## final  value 75954.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 78256.062481 
## iter  10 value 75959.647696
## iter  20 value 75958.824655
## final  value 75958.798217 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80229.225134 
## iter  10 value 75962.549090
## iter  20 value 75961.981242
## iter  30 value 75958.804917
## final  value 75958.798543 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 79242.735199 
## iter  10 value 75958.828138
## final  value 75958.798270 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 79807.963058 
## iter  10 value 75962.495680
## iter  20 value 75958.852021
## final  value 75958.798428 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 78999.106268 
## iter  10 value 75963.225004
## iter  20 value 75958.937762
## iter  30 value 75958.798721
## iter  30 value 75958.798475
## iter  30 value 75958.798445
## final  value 75958.798445 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 80415.786855 
## iter  10 value 75958.192559
## iter  20 value 75957.016945
## final  value 75956.720278 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80997.970650 
## iter  10 value 75959.715001
## iter  20 value 75957.282738
## iter  30 value 75956.760064
## final  value 75956.718119 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79442.325964 
## iter  10 value 75962.538469
## iter  20 value 75957.293340
## final  value 75956.718633 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 80105.254695 
## iter  10 value 75959.239159
## iter  20 value 75957.175579
## iter  30 value 75956.720601
## iter  30 value 75956.720360
## iter  30 value 75956.720360
## final  value 75956.720360 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 78916.581213 
## iter  10 value 75968.098032
## iter  20 value 75956.985042
## iter  30 value 75956.734791
## final  value 75956.717815 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79815.852283 
## iter  10 value 75977.983065
## iter  20 value 75956.988864
## iter  30 value 75956.029494
## iter  40 value 75955.951445
## final  value 75955.944765 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 78603.806648 
## iter  10 value 75992.789620
## iter  20 value 75956.441564
## iter  30 value 75955.963915
## final  value 75955.944174 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 79492.127717 
## iter  10 value 76035.559763
## iter  20 value 75956.907059
## iter  30 value 75956.802129
## iter  40 value 75956.639304
## iter  50 value 75956.522430
## iter  60 value 75956.492301
## iter  70 value 75955.978191
## iter  80 value 75955.949086
## final  value 75955.944043 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 80115.098672 
## iter  10 value 75959.480002
## iter  20 value 75958.653081
## iter  30 value 75956.932858
## iter  40 value 75956.111013
## iter  50 value 75956.046862
## iter  60 value 75956.012310
## iter  60 value 75956.011636
## final  value 75955.946659 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 80192.081945 
## iter  10 value 75962.577678
## iter  20 value 75956.114652
## iter  30 value 75955.945052
## final  value 75955.943692 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79744.323442 
## iter  10 value 76000.690837
## iter  20 value 75954.538309
## final  value 75954.083778 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 78168.270909 
## iter  10 value 75963.571460
## iter  20 value 75954.110351
## final  value 75954.024218 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80350.235041 
## iter  10 value 75994.078417
## iter  20 value 75954.462073
## final  value 75954.086765 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 78409.362851 
## iter  10 value 75975.021018
## iter  20 value 75954.242356
## final  value 75954.013517 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80018.643958 
## iter  10 value 75965.225015
## iter  20 value 75954.129416
## final  value 75954.023424 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 80482.677448 
## iter  10 value 75978.463558
## iter  20 value 75954.282046
## iter  30 value 75954.015564
## iter  30 value 75954.015558
## iter  30 value 75954.015552
## final  value 75954.015552 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79841.235803 
## iter  10 value 75986.713814
## iter  20 value 75954.377165
## final  value 75954.053593 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79419.098940 
## iter  10 value 75999.750119
## iter  20 value 75954.527463
## iter  30 value 75954.012722
## iter  30 value 75954.012686
## final  value 75954.012686 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 77500.682377 
## iter  10 value 75973.134481
## iter  20 value 75954.220606
## final  value 75954.008237 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 78574.920972 
## iter  10 value 75995.424116
## iter  20 value 75954.477588
## iter  30 value 75954.015645
## iter  30 value 75954.014952
## iter  30 value 75954.014567
## final  value 75954.014567 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80262.079173 
## iter  10 value 75974.466847
## iter  20 value 75954.235967
## final  value 75954.045795 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79153.274669 
## iter  10 value 76016.706466
## iter  20 value 75954.722956
## final  value 75954.031575 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 80831.120892 
## iter  10 value 75998.104384
## iter  20 value 75954.508489
## final  value 75954.010780 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 79651.931010 
## iter  10 value 76021.341479
## iter  20 value 75954.776394
## final  value 75954.033499 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 78932.285173 
## iter  10 value 75992.809876
## iter  20 value 75954.447447
## final  value 75954.030213 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 67778.167243 
## final  value 63935.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 67392.284708 
## final  value 63935.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 67486.374288 
## final  value 63935.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 66446.604946 
## final  value 63935.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 68057.404922 
## final  value 63935.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 66442.403766 
## final  value 63935.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 67937.249533 
## final  value 63935.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 66135.015060 
## final  value 63935.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 66915.737189 
## final  value 63935.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 66822.061033 
## final  value 63935.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 67422.975620 
## final  value 63935.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 68235.659749 
## final  value 63935.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 67606.676772 
## final  value 63935.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 64951.094282 
## final  value 63935.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 66780.927080 
## final  value 63935.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 66731.042393 
## iter  10 value 63943.010562
## iter  20 value 63939.797562
## iter  30 value 63939.775094
## iter  30 value 63939.774874
## iter  30 value 63939.774874
## final  value 63939.774874 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 66609.798672 
## iter  10 value 63943.367372
## final  value 63939.774601 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 68703.118853 
## iter  10 value 63943.925603
## iter  20 value 63940.774469
## final  value 63939.774819 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 67540.918664 
## iter  10 value 63940.227694
## final  value 63939.774787 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 68069.073844 
## iter  10 value 63943.429535
## iter  20 value 63939.788465
## iter  20 value 63939.787956
## final  value 63939.774597 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 67592.343373 
## iter  10 value 63939.410394
## iter  20 value 63938.503637
## iter  30 value 63937.736198
## final  value 63937.706551 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 65834.746889 
## iter  10 value 63962.021312
## iter  20 value 63939.042556
## iter  30 value 63937.731760
## final  value 63937.705480 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 65717.829000 
## iter  10 value 63966.058683
## iter  20 value 63938.111250
## iter  30 value 63937.785091
## iter  40 value 63937.705530
## iter  40 value 63937.705414
## iter  40 value 63937.705414
## final  value 63937.705414 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 65664.162760 
## iter  10 value 63938.669176
## iter  20 value 63937.721509
## final  value 63937.705919 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 66195.633140 
## iter  10 value 63940.015935
## iter  20 value 63938.299104
## iter  30 value 63937.708605
## final  value 63937.705440 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 66233.118040 
## iter  10 value 63960.435858
## iter  20 value 63937.861397
## iter  30 value 63937.279209
## iter  30 value 63937.278856
## final  value 63937.251045 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 65799.020339 
## iter  10 value 63949.698634
## iter  20 value 63937.049683
## iter  30 value 63936.937290
## final  value 63936.935677 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 67936.024161 
## iter  10 value 63938.723499
## iter  20 value 63937.023763
## final  value 63936.935006 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 64695.361400 
## iter  10 value 63938.680174
## iter  20 value 63937.044265
## iter  30 value 63936.937014
## iter  30 value 63936.936527
## iter  30 value 63936.936299
## final  value 63936.936299 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 67177.339224 
## iter  10 value 63939.445924
## iter  20 value 63937.037536
## iter  30 value 63936.945359
## final  value 63936.936090 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 67545.943361 
## iter  10 value 63946.387215
## iter  20 value 63935.131286
## final  value 63935.021666 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 65792.903635 
## iter  10 value 63950.436532
## iter  20 value 63935.177971
## final  value 63935.011365 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 68814.514534 
## iter  10 value 63947.469025
## iter  20 value 63935.143758
## final  value 63935.011834 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 67622.655910 
## iter  10 value 63946.192879
## iter  20 value 63935.129045
## final  value 63935.019881 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 67911.028931 
## iter  10 value 63955.230996
## iter  20 value 63935.233248
## final  value 63935.013263 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 66822.382590 
## iter  10 value 63962.430456
## iter  20 value 63935.316252
## final  value 63935.016348 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 68115.695058 
## iter  10 value 63977.612994
## iter  20 value 63935.491294
## final  value 63935.027295 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 67500.799448 
## iter  10 value 63965.115687
## iter  20 value 63935.347210
## final  value 63935.019935 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 67362.954927 
## iter  10 value 63986.374387
## iter  20 value 63935.592306
## final  value 63935.067875 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 67170.586727 
## iter  10 value 63987.841880
## iter  20 value 63935.609225
## iter  30 value 63935.014321
## iter  30 value 63935.014316
## iter  30 value 63935.014310
## final  value 63935.014310 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 67284.905303 
## iter  10 value 63978.133607
## iter  20 value 63935.497297
## final  value 63935.039745 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 66955.269692 
## iter  10 value 63962.922246
## iter  20 value 63935.321922
## final  value 63935.025596 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 68227.122738 
## iter  10 value 63964.876531
## iter  20 value 63935.344453
## final  value 63935.028020 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 67878.556286 
## iter  10 value 63994.709007
## iter  20 value 63935.688398
## iter  30 value 63935.022532
## final  value 63935.014899 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 68266.458125 
## iter  10 value 63960.792650
## iter  20 value 63935.297369
## final  value 63935.020101 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80703.688245 
## final  value 78012.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 81782.649256 
## final  value 78012.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 81971.623802 
## final  value 78012.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80465.686314 
## final  value 78012.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80480.059502 
## final  value 78012.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 80283.152400 
## final  value 78012.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80666.799718 
## final  value 78012.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 81902.382342 
## final  value 78012.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 81705.400078 
## final  value 78012.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 81486.617096 
## final  value 78012.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80388.155434 
## final  value 78012.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 82507.193676 
## final  value 78012.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 79949.949646 
## final  value 78012.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 83260.620010 
## final  value 78012.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 80041.524815 
## final  value 78012.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 81354.747730 
## iter  10 value 78016.832757
## final  value 78016.813048 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 81093.812951 
## iter  10 value 78025.383502
## iter  20 value 78016.816014
## final  value 78016.812485 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80198.295442 
## iter  10 value 78016.871672
## final  value 78016.812860 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80675.246752 
## iter  10 value 78018.461474
## iter  20 value 78016.816904
## final  value 78016.815709 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 82262.207742 
## iter  10 value 78028.386090
## iter  20 value 78017.205188
## iter  30 value 78016.817304
## iter  30 value 78016.816741
## final  value 78016.813087 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 80970.322040 
## iter  10 value 78017.404892
## iter  20 value 78014.873351
## iter  30 value 78014.730933
## final  value 78014.725605 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 81389.038276 
## iter  10 value 78015.568925
## iter  20 value 78014.731577
## final  value 78014.726214 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 81905.760399 
## iter  10 value 78022.523543
## iter  20 value 78015.393007
## iter  30 value 78014.729526
## final  value 78014.726021 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 82319.516807 
## iter  10 value 78034.318700
## iter  20 value 78014.848430
## iter  30 value 78014.727810
## final  value 78014.725584 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 81682.887530 
## iter  10 value 78040.236135
## iter  20 value 78015.719806
## iter  30 value 78015.008226
## iter  40 value 78014.730943
## final  value 78014.726214 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 81034.605189 
## iter  10 value 78025.412359
## iter  20 value 78015.257363
## iter  30 value 78014.295491
## iter  40 value 78014.274145
## iter  40 value 78014.274042
## final  value 78014.267237 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80312.481517 
## iter  10 value 78023.286110
## iter  20 value 78019.248675
## iter  30 value 78018.308047
## iter  40 value 78014.052802
## iter  50 value 78013.981603
## iter  50 value 78013.980866
## final  value 78013.949195 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 82137.113038 
## iter  10 value 78017.668007
## iter  20 value 78014.089405
## iter  30 value 78013.990094
## final  value 78013.949168 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 81865.173011 
## iter  10 value 78051.846500
## iter  20 value 78016.049553
## iter  30 value 78014.427327
## iter  40 value 78014.276398
## iter  50 value 78014.167745
## iter  60 value 78014.103936
## iter  70 value 78014.080526
## final  value 78013.959106 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 79973.177670 
## iter  10 value 78047.943724
## iter  20 value 78019.494817
## iter  30 value 78014.762975
## iter  40 value 78013.958239
## final  value 78013.949434 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80659.575451 
## iter  10 value 78034.676496
## iter  20 value 78012.261442
## final  value 78012.014066 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 81351.422481 
## iter  10 value 78034.224742
## iter  20 value 78012.256234
## final  value 78012.042584 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 82298.824434 
## iter  10 value 78032.665574
## iter  20 value 78012.238258
## final  value 78012.013218 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81713.716641 
## iter  10 value 78082.421980
## iter  20 value 78012.811910
## final  value 78012.112534 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 82338.068361 
## iter  10 value 78023.365687
## iter  20 value 78012.131037
## final  value 78012.025853 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81684.770398 
## iter  10 value 78060.737223
## iter  20 value 78012.561902
## iter  30 value 78012.014765
## iter  30 value 78012.014759
## iter  30 value 78012.014753
## final  value 78012.014753 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 81485.122131 
## iter  10 value 78024.744382
## iter  20 value 78012.146933
## final  value 78012.024249 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 82814.904631 
## iter  10 value 78041.003434
## iter  20 value 78012.334387
## final  value 78012.010921 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 82553.388700 
## iter  10 value 78037.388539
## iter  20 value 78012.292710
## final  value 78012.015642 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80573.356173 
## iter  10 value 78049.294880
## iter  20 value 78012.429981
## final  value 78012.017167 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 82679.069080 
## iter  10 value 78067.288832
## iter  20 value 78012.637437
## final  value 78012.031950 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 82555.417150 
## iter  10 value 78061.645565
## iter  20 value 78012.572374
## final  value 78012.055148 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 81877.033215 
## iter  10 value 78082.679508
## iter  20 value 78012.814879
## final  value 78012.035710 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 82202.884247 
## iter  10 value 78067.326856
## iter  20 value 78012.637875
## final  value 78012.027596 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 81630.173530 
## iter  10 value 78079.362683
## iter  20 value 78012.776639
## final  value 78012.052761 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80093.835850 
## final  value 76870.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80652.879822 
## final  value 76870.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80660.586802 
## final  value 76870.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 79529.199266 
## final  value 76870.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 81213.969829 
## final  value 76870.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 78969.651282 
## final  value 76870.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79521.814147 
## final  value 76870.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 81369.773760 
## final  value 76870.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 80559.146633 
## final  value 76870.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 79816.346219 
## final  value 76870.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79877.950789 
## final  value 76870.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80423.512477 
## final  value 76870.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 78260.102466 
## final  value 76870.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 81019.215149 
## final  value 76870.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 80313.322556 
## final  value 76870.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79590.846379 
## iter  10 value 76875.191003
## iter  20 value 76874.806981
## iter  20 value 76874.806434
## iter  20 value 76874.806414
## final  value 76874.806414 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80018.875034 
## iter  10 value 76875.072313
## iter  20 value 76874.807086
## iter  20 value 76874.806475
## iter  20 value 76874.806475
## final  value 76874.806475 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80839.110965 
## iter  10 value 76883.376734
## iter  20 value 76874.806644
## iter  20 value 76874.806392
## iter  20 value 76874.805908
## final  value 76874.805908 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 79917.013855 
## iter  10 value 76877.005088
## final  value 76874.806049 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 79960.277115 
## iter  10 value 76875.060388
## final  value 76874.805715 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 78429.514074 
## iter  10 value 76875.337872
## iter  20 value 76872.763136
## final  value 76872.722082 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 81672.947183 
## iter  10 value 76890.132035
## iter  20 value 76873.026376
## iter  30 value 76872.750835
## final  value 76872.723710 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79518.219628 
## iter  10 value 76893.550725
## iter  20 value 76872.738316
## final  value 76872.722473 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79954.398124 
## iter  10 value 76896.881105
## iter  20 value 76874.068000
## iter  30 value 76872.960350
## final  value 76872.722263 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 81177.955214 
## iter  10 value 76881.290792
## iter  20 value 76874.967818
## iter  30 value 76872.743931
## final  value 76872.722238 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79079.549818 
## iter  10 value 76876.255013
## iter  20 value 76872.564674
## iter  30 value 76872.198646
## iter  40 value 76871.952785
## final  value 76871.946685 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80802.266281 
## iter  10 value 76874.572159
## iter  20 value 76872.022076
## final  value 76871.946902 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 81572.141008 
## iter  10 value 76906.124606
## iter  20 value 76872.351557
## iter  30 value 76872.116017
## iter  40 value 76871.963683
## final  value 76871.947188 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 79574.296111 
## iter  10 value 76895.216687
## iter  20 value 76872.753663
## iter  30 value 76872.129551
## iter  40 value 76871.952155
## final  value 76871.946623 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 80656.945632 
## iter  10 value 76893.081789
## iter  20 value 76872.839610
## iter  30 value 76872.278260
## iter  40 value 76871.951533
## final  value 76871.946528 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79912.213356 
## iter  10 value 76908.278314
## iter  20 value 76870.441319
## final  value 76870.068104 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80129.026623 
## iter  10 value 76882.246806
## iter  20 value 76870.141196
## final  value 76870.023302 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 81063.116818 
## iter  10 value 76880.669196
## iter  20 value 76870.123007
## final  value 76870.024157 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80931.400388 
## iter  10 value 76881.743982
## iter  20 value 76870.135399
## final  value 76870.018979 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80901.042559 
## iter  10 value 76892.108837
## iter  20 value 76870.254898
## final  value 76870.044139 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 79098.920915 
## iter  10 value 76895.145237
## iter  20 value 76870.289905
## final  value 76870.015122 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79622.801161 
## iter  10 value 76904.068083
## iter  20 value 76870.392778
## final  value 76870.026585 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79146.605801 
## iter  10 value 76903.942838
## iter  20 value 76870.391334
## final  value 76870.024758 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79962.570038 
## iter  10 value 76887.772944
## iter  20 value 76870.204908
## final  value 76870.025321 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 79194.353921 
## iter  10 value 76881.817329
## iter  20 value 76870.136245
## final  value 76870.026313 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80980.926584 
## iter  10 value 76932.945219
## iter  20 value 76870.725709
## final  value 76870.049224 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80559.859250 
## iter  10 value 76917.064675
## iter  20 value 76870.542619
## iter  30 value 76870.011855
## iter  30 value 76870.011850
## iter  30 value 76870.011845
## final  value 76870.011845 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 81662.332062 
## iter  10 value 76910.761951
## iter  20 value 76870.469953
## final  value 76870.018737 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 79599.103635 
## iter  10 value 76901.530876
## iter  20 value 76870.363526
## final  value 76870.015727 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 79527.363328 
## iter  10 value 76916.305462
## iter  20 value 76870.533866
## iter  30 value 76870.026518
## final  value 76870.025426 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 65598.046058 
## final  value 61909.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 65413.479687 
## final  value 61909.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 65488.323758 
## final  value 61909.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 64759.659281 
## final  value 61909.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 64090.219126 
## final  value 61909.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 64957.732732 
## final  value 61909.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 63266.598297 
## final  value 61909.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 66039.502561 
## final  value 61909.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 64194.575436 
## final  value 61909.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 65264.882725 
## final  value 61909.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 64497.730700 
## final  value 61909.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 65929.895360 
## final  value 61909.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 64016.737825 
## final  value 61909.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 65283.534492 
## final  value 61909.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 63616.704583 
## final  value 61909.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 64584.548927 
## iter  10 value 61914.004011
## iter  20 value 61913.762932
## final  value 61913.761358 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 64448.015214 
## iter  10 value 61914.768527
## final  value 61913.760982 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 64028.207369 
## iter  10 value 61916.290404
## iter  20 value 61913.810824
## final  value 61913.761033 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 64246.187759 
## iter  10 value 61918.566049
## iter  20 value 61913.778295
## final  value 61913.760938 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 65174.543388 
## iter  10 value 61918.384447
## iter  20 value 61913.772222
## final  value 61913.761595 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 66027.885156 
## iter  10 value 61931.762657
## iter  20 value 61911.872549
## iter  30 value 61911.702432
## iter  30 value 61911.702021
## final  value 61911.700638 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 66144.362696 
## iter  10 value 61913.824633
## iter  20 value 61912.234847
## iter  30 value 61911.700628
## iter  30 value 61911.700400
## final  value 61911.698271 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 64699.464876 
## iter  10 value 61913.446443
## iter  20 value 61911.706725
## final  value 61911.698795 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 64630.882510 
## iter  10 value 61928.881881
## iter  20 value 61911.824564
## final  value 61911.698346 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 64534.435865 
## iter  10 value 61918.726187
## iter  20 value 61911.895419
## final  value 61911.698673 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 64274.916740 
## iter  10 value 61954.054970
## iter  20 value 61911.156198
## iter  30 value 61910.930219
## iter  30 value 61910.929944
## iter  30 value 61910.929872
## final  value 61910.929872 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 63485.877206 
## iter  10 value 61913.533926
## iter  20 value 61911.011513
## iter  30 value 61910.933674
## final  value 61910.930069 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 64494.475587 
## iter  10 value 61957.344446
## iter  20 value 61911.206384
## iter  30 value 61910.937520
## final  value 61910.929789 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 64900.256390 
## iter  10 value 61955.497696
## iter  20 value 61911.143398
## iter  30 value 61910.950286
## final  value 61910.931686 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 64908.990210 
## iter  10 value 61919.971399
## iter  20 value 61911.710233
## iter  30 value 61911.039903
## final  value 61910.931211 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 65672.950505 
## iter  10 value 61928.860715
## iter  20 value 61909.228978
## final  value 61909.042311 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 65563.179521 
## iter  10 value 61930.318929
## iter  20 value 61909.245791
## final  value 61909.012193 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 66489.338299 
## iter  10 value 61928.567268
## iter  20 value 61909.225595
## final  value 61909.058776 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 64295.409454 
## iter  10 value 61950.573369
## iter  20 value 61909.479308
## final  value 61909.029965 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 65722.510477 
## iter  10 value 61929.392820
## iter  20 value 61909.235113
## final  value 61909.043805 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 66498.282374 
## iter  10 value 61931.495510
## iter  20 value 61909.259356
## final  value 61909.036192 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 63817.021119 
## iter  10 value 61934.444999
## iter  20 value 61909.293361
## iter  30 value 61909.010168
## iter  30 value 61909.009927
## final  value 61909.008552 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 63834.548052 
## iter  10 value 61924.873568
## iter  20 value 61909.183010
## final  value 61909.014365 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 63006.752249 
## iter  10 value 61921.201360
## iter  20 value 61909.140672
## final  value 61909.009749 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 65102.412893 
## iter  10 value 61923.796650
## iter  20 value 61909.170594
## final  value 61909.018018 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 63642.721488 
## iter  10 value 61944.012228
## iter  20 value 61909.403664
## final  value 61909.020387 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 64587.544594 
## iter  10 value 61942.519014
## iter  20 value 61909.386448
## iter  30 value 61909.009239
## iter  30 value 61909.009092
## iter  30 value 61909.009088
## final  value 61909.009088 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 65166.905958 
## iter  10 value 61952.750632
## iter  20 value 61909.504410
## final  value 61909.081464 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 63640.939419 
## iter  10 value 61937.123567
## iter  20 value 61909.324243
## iter  30 value 61909.010459
## iter  30 value 61909.009925
## final  value 61909.008070 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 64768.834799 
## iter  10 value 61912.769776
## iter  20 value 61909.114989
## final  value 61909.042985 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 78466.765311 
## final  value 76429.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80526.843422 
## final  value 76429.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80090.941386 
## final  value 76429.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 78937.065195 
## final  value 76429.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80424.585805 
## final  value 76429.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 78108.836069 
## final  value 76429.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79326.048771 
## final  value 76429.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 78402.729226 
## final  value 76429.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 80699.696433 
## final  value 76429.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 78883.432259 
## final  value 76429.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80957.552142 
## final  value 76429.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80160.286124 
## final  value 76429.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 78903.678742 
## final  value 76429.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 78793.100476 
## final  value 76429.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 78052.335184 
## final  value 76429.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79405.305446 
## iter  10 value 76433.991825
## iter  20 value 76433.797912
## iter  20 value 76433.797362
## iter  20 value 76433.796939
## final  value 76433.796939 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 78735.970962 
## iter  10 value 76549.130167
## iter  20 value 76433.797080
## final  value 76433.796190 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80346.602554 
## iter  10 value 76434.318618
## final  value 76433.796178 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 79874.501871 
## iter  10 value 76446.582533
## iter  20 value 76433.864415
## final  value 76433.796377 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 78798.733467 
## iter  10 value 76437.424964
## iter  20 value 76434.015801
## iter  30 value 76433.796403
## iter  30 value 76433.796210
## iter  30 value 76433.796194
## final  value 76433.796194 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 80243.902039 
## iter  10 value 76435.424837
## iter  20 value 76431.731017
## iter  20 value 76431.730519
## final  value 76431.717367 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79817.092479 
## iter  10 value 76447.968369
## iter  20 value 76432.484389
## iter  30 value 76431.726702
## iter  30 value 76431.726397
## final  value 76431.716838 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 80288.250609 
## iter  10 value 76443.140494
## iter  20 value 76432.562394
## iter  30 value 76432.007921
## iter  40 value 76431.732927
## final  value 76431.716917 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79264.064534 
## iter  10 value 76433.589058
## iter  20 value 76431.844727
## iter  30 value 76431.719153
## final  value 76431.716990 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80283.568707 
## iter  10 value 76439.374840
## iter  20 value 76433.746191
## iter  30 value 76431.844955
## final  value 76431.716843 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80916.637652 
## iter  10 value 76432.133425
## iter  20 value 76431.709864
## iter  30 value 76430.954104
## final  value 76430.943569 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79085.750610 
## iter  10 value 76437.119641
## iter  20 value 76430.965964
## final  value 76430.943657 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 80134.055899 
## iter  10 value 76437.791531
## iter  20 value 76432.581081
## iter  30 value 76431.556621
## iter  40 value 76431.201060
## iter  50 value 76430.983415
## final  value 76430.942840 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 78539.377307 
## iter  10 value 76436.545839
## iter  20 value 76432.633786
## iter  30 value 76431.789631
## iter  40 value 76431.164622
## iter  50 value 76430.943513
## iter  50 value 76430.943230
## iter  50 value 76430.943230
## final  value 76430.943230 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 81556.247399 
## iter  10 value 76432.676518
## iter  20 value 76431.066266
## final  value 76430.944989 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79515.107185 
## iter  10 value 76441.454625
## iter  20 value 76429.143592
## final  value 76429.016500 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 79654.827812 
## iter  10 value 76444.952726
## iter  20 value 76429.183922
## final  value 76429.030353 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 79542.465980 
## iter  10 value 76452.331420
## iter  20 value 76429.268993
## final  value 76429.011637 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80620.077620 
## iter  10 value 76440.283326
## iter  20 value 76429.130088
## final  value 76429.025077 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 79081.329946 
## iter  10 value 76451.130132
## iter  20 value 76429.255143
## final  value 76429.014288 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 78824.852528 
## iter  10 value 76439.272413
## iter  20 value 76429.118433
## final  value 76429.022911 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79855.364170 
## iter  10 value 76453.573324
## iter  20 value 76429.283311
## iter  30 value 76429.009797
## iter  30 value 76429.009739
## iter  30 value 76429.009717
## final  value 76429.009717 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79148.289991 
## iter  10 value 76471.904309
## iter  20 value 76429.494653
## final  value 76429.021677 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 80386.733398 
## iter  10 value 76440.112786
## iter  20 value 76429.128122
## final  value 76429.024207 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 79344.145531 
## iter  10 value 76452.405590
## iter  20 value 76429.269848
## iter  30 value 76429.011992
## iter  30 value 76429.011988
## iter  30 value 76429.011983
## final  value 76429.011983 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80141.635283 
## iter  10 value 76473.498186
## iter  20 value 76429.513029
## final  value 76429.021012 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 81021.167300 
## iter  10 value 76449.606526
## iter  20 value 76429.237577
## final  value 76429.052401 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 77641.398480 
## iter  10 value 76447.613966
## iter  20 value 76429.214604
## final  value 76429.013369 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 77862.344123 
## iter  10 value 76446.886850
## iter  20 value 76429.206221
## final  value 76429.009071 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 80108.761805 
## iter  10 value 76478.752979
## iter  20 value 76429.573613
## final  value 76429.015991 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 82084.735259 
## final  value 78495.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80796.729801 
## final  value 78495.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80748.633120 
## final  value 78495.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81712.679474 
## final  value 78495.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 81247.020208 
## final  value 78495.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 80530.356974 
## final  value 78495.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 83628.033281 
## final  value 78495.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 80632.065383 
## final  value 78495.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 83339.309478 
## final  value 78495.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 82055.598578 
## final  value 78495.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80602.167121 
## final  value 78495.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79851.916757 
## final  value 78495.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 83506.238930 
## final  value 78495.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 81009.061164 
## final  value 78495.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 80793.121421 
## final  value 78495.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80222.510567 
## iter  10 value 78500.953156
## final  value 78499.815164 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 81424.883379 
## iter  10 value 78503.539347
## final  value 78499.815192 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80902.384137 
## iter  10 value 78505.701259
## iter  20 value 78499.872520
## final  value 78499.815502 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81488.900126 
## iter  10 value 78561.509701
## iter  20 value 78503.449758
## iter  30 value 78499.817153
## final  value 78499.815347 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 81621.957126 
## iter  10 value 78617.843770
## iter  20 value 78499.972877
## final  value 78499.815399 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81848.502907 
## iter  10 value 78502.097557
## iter  20 value 78497.989491
## iter  30 value 78497.734076
## final  value 78497.727312 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 82972.848783 
## iter  10 value 78499.587026
## iter  20 value 78497.957266
## iter  30 value 78497.731044
## final  value 78497.727235 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 81176.048300 
## iter  10 value 78502.507834
## iter  20 value 78498.482301
## final  value 78498.455535 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 81111.419218 
## iter  10 value 78498.596816
## iter  20 value 78497.738187
## final  value 78497.727462 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 82837.226700 
## iter  10 value 78501.725995
## iter  20 value 78497.835397
## iter  30 value 78497.730688
## iter  30 value 78497.730019
## final  value 78497.726956 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 81436.531999 
## iter  10 value 78502.816690
## iter  20 value 78497.577420
## iter  30 value 78496.972561
## final  value 78496.950369 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 82043.049572 
## iter  10 value 78500.954677
## iter  20 value 78497.334385
## iter  30 value 78497.059303
## iter  40 value 78496.956423
## final  value 78496.951306 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 82508.693314 
## iter  10 value 78514.211193
## iter  20 value 78498.112819
## iter  30 value 78496.997170
## final  value 78496.951889 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 80271.373241 
## iter  10 value 78533.609251
## iter  20 value 78497.008129
## iter  30 value 78496.950507
## iter  30 value 78496.950391
## iter  30 value 78496.950044
## final  value 78496.950044 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 80503.854723 
## iter  10 value 78519.069614
## iter  20 value 78497.723099
## iter  30 value 78497.113689
## iter  40 value 78496.989561
## final  value 78496.957433 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 81683.629946 
## iter  10 value 78507.426273
## iter  20 value 78495.143265
## final  value 78495.021790 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 81698.316374 
## iter  10 value 78519.433423
## iter  20 value 78495.281698
## final  value 78495.043699 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80751.811710 
## iter  10 value 78507.094338
## iter  20 value 78495.139438
## final  value 78495.027757 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81025.317679 
## iter  10 value 78507.830969
## iter  20 value 78495.147931
## final  value 78495.018021 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 82008.747391 
## iter  10 value 78507.455380
## iter  20 value 78495.143601
## final  value 78495.021841 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 80639.617792 
## iter  10 value 78540.725703
## iter  20 value 78495.527181
## final  value 78495.022895 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 83575.423198 
## iter  10 value 78520.169408
## iter  20 value 78495.290184
## final  value 78495.015991 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 80159.886531 
## iter  10 value 78520.144769
## iter  20 value 78495.289899
## final  value 78495.018060 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 82689.079063 
## iter  10 value 78566.595853
## iter  20 value 78495.825444
## final  value 78495.048663 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 82433.378251 
## iter  10 value 78537.393031
## iter  20 value 78495.488758
## final  value 78495.013313 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 82066.874325 
## iter  10 value 78544.395135
## iter  20 value 78495.569487
## final  value 78495.015767 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 83303.062750 
## iter  10 value 78578.315928
## iter  20 value 78495.960567
## final  value 78495.071555 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 83037.009883 
## iter  10 value 78548.055233
## iter  20 value 78495.611685
## final  value 78495.032838 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 80965.460984 
## iter  10 value 78534.541006
## iter  20 value 78495.455877
## final  value 78495.007575 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 80325.846880 
## iter  10 value 78529.453934
## iter  20 value 78495.397227
## final  value 78495.021163 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79062.842348 
## final  value 74549.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 78218.934368 
## final  value 74549.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 78104.871548 
## final  value 74549.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 78342.019398 
## final  value 74549.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 78022.273693 
## final  value 74549.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 78163.643147 
## final  value 74549.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 78890.659320 
## final  value 74549.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 77340.115889 
## final  value 74549.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 77451.626194 
## final  value 74549.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 77489.018620 
## final  value 74549.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 77539.889808 
## final  value 74549.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 77106.692446 
## final  value 74549.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 79787.930533 
## final  value 74549.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 76384.964586 
## final  value 74549.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 78413.787065 
## final  value 74549.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 75926.258596 
## iter  10 value 74555.240629
## final  value 74553.792753 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 77144.036535 
## iter  10 value 74554.184278
## final  value 74553.792924 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 78063.456072 
## iter  10 value 74557.366974
## iter  20 value 74553.818969
## final  value 74553.793112 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 77514.700376 
## iter  10 value 74557.000799
## iter  20 value 74553.825691
## final  value 74553.793191 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 77254.743398 
## iter  10 value 74557.156282
## iter  20 value 74553.865414
## final  value 74553.793630 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 77549.817818 
## iter  10 value 74554.030254
## iter  20 value 74551.781630
## final  value 74551.715213 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 78177.595837 
## iter  10 value 74572.819182
## iter  20 value 74554.109633
## iter  30 value 74553.481141
## iter  40 value 74551.718609
## final  value 74551.715519 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 76764.568494 
## iter  10 value 74556.886828
## iter  20 value 74553.341405
## iter  30 value 74553.186790
## iter  40 value 74551.829011
## iter  50 value 74551.725051
## final  value 74551.715145 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 77498.043718 
## iter  10 value 74566.993114
## iter  20 value 74553.651384
## iter  30 value 74552.509821
## iter  40 value 74552.409712
## iter  50 value 74551.754254
## final  value 74551.715438 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 77022.296024 
## iter  10 value 74557.148653
## iter  20 value 74552.479004
## iter  30 value 74551.739022
## final  value 74551.715092 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 78545.375345 
## iter  10 value 74552.418218
## iter  20 value 74551.114759
## iter  30 value 74550.994322
## iter  40 value 74550.943475
## iter  40 value 74550.943274
## final  value 74550.941718 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 78622.146632 
## iter  10 value 74553.097629
## iter  20 value 74551.243419
## iter  30 value 74550.942945
## iter  30 value 74550.942451
## iter  30 value 74550.942440
## final  value 74550.942440 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 76787.582094 
## iter  10 value 74585.967399
## iter  20 value 74550.969279
## final  value 74550.942745 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 78312.956262 
## iter  10 value 74560.171898
## iter  20 value 74551.265615
## iter  30 value 74551.154776
## iter  40 value 74550.964612
## final  value 74550.953409 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 77440.409842 
## iter  10 value 74592.860923
## iter  20 value 74551.446815
## iter  30 value 74551.085252
## iter  40 value 74550.950849
## iter  40 value 74550.950339
## final  value 74550.941570 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 77964.867136 
## iter  10 value 74572.765110
## iter  20 value 74549.273993
## final  value 74549.013330 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 76910.192406 
## iter  10 value 74568.347324
## iter  20 value 74549.223059
## final  value 74549.011803 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 77326.791587 
## iter  10 value 74561.318026
## iter  20 value 74549.142017
## final  value 74549.026152 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 77030.002082 
## iter  10 value 74570.052395
## iter  20 value 74549.242718
## final  value 74549.012082 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 77933.551029 
## iter  10 value 74572.796165
## iter  20 value 74549.274351
## final  value 74549.017377 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 77741.594167 
## iter  10 value 74563.145206
## iter  20 value 74549.163083
## final  value 74549.026914 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 77025.950899 
## iter  10 value 74581.585978
## iter  20 value 74549.375691
## final  value 74549.025256 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 78664.288349 
## iter  10 value 74580.942250
## iter  20 value 74549.368269
## final  value 74549.014735 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 78599.839348 
## iter  10 value 74600.547177
## iter  20 value 74549.594298
## final  value 74549.066259 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 77672.667165 
## iter  10 value 74584.216990
## iter  20 value 74549.406024
## final  value 74549.010379 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 77708.962122 
## iter  10 value 74594.566336
## iter  20 value 74549.525344
## iter  30 value 74549.012559
## iter  30 value 74549.012359
## iter  30 value 74549.012354
## final  value 74549.012354 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 76617.473063 
## iter  10 value 74550.929496
## final  value 74549.082121 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 77729.694559 
## iter  10 value 74552.423337
## final  value 74549.169041 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 77689.829635 
## iter  10 value 74553.607759
## iter  20 value 74549.147421
## final  value 74549.037221 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 78219.589123 
## iter  10 value 74604.497312
## iter  20 value 74549.639840
## final  value 74549.025921 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80784.768791 
## final  value 77286.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80314.811508 
## final  value 77286.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 81134.073958 
## final  value 77286.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80058.113350 
## final  value 77286.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 81380.546347 
## final  value 77286.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81232.213383 
## final  value 77286.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 81648.798376 
## final  value 77286.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 80695.630711 
## final  value 77286.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79147.700872 
## final  value 77286.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80927.790508 
## final  value 77286.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 81558.738334 
## final  value 77286.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79552.706998 
## final  value 77286.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 82263.939208 
## final  value 77286.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 79643.015839 
## final  value 77286.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 81051.678357 
## final  value 77286.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 81613.848727 
## iter  10 value 77294.184791
## iter  20 value 77291.012298
## iter  30 value 77290.823404
## final  value 77290.813052 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 79408.546424 
## iter  10 value 77291.316741
## iter  20 value 77290.835434
## final  value 77290.813243 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 79677.954672 
## iter  10 value 77291.335858
## iter  20 value 77290.960094
## iter  30 value 77290.813626
## iter  30 value 77290.813294
## iter  30 value 77290.813227
## final  value 77290.813227 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80197.657401 
## iter  10 value 77295.288016
## iter  20 value 77290.933415
## final  value 77290.813204 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 81153.609157 
## iter  10 value 77296.052122
## iter  20 value 77290.816173
## final  value 77290.813689 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81139.646778 
## iter  10 value 77290.355312
## iter  20 value 77288.781354
## final  value 77288.726172 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80113.769714 
## iter  10 value 77292.908860
## iter  20 value 77289.315552
## iter  30 value 77288.741074
## final  value 77288.726283 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 80450.060178 
## iter  10 value 77471.881535
## iter  20 value 77293.753263
## iter  30 value 77289.636183
## iter  40 value 77288.791272
## final  value 77288.725988 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 82011.451337 
## iter  10 value 77291.311185
## final  value 77288.726209 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 79002.383601 
## iter  10 value 77290.304625
## iter  20 value 77288.750886
## final  value 77288.725944 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 81460.644637 
## iter  10 value 77294.391979
## iter  20 value 77291.949583
## iter  30 value 77289.802693
## iter  40 value 77288.007868
## iter  50 value 77287.978330
## final  value 77287.949190 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 82416.578786 
## iter  10 value 77323.784988
## iter  20 value 77288.256575
## iter  30 value 77287.977624
## final  value 77287.952217 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 81328.543113 
## iter  10 value 77329.090771
## iter  20 value 77323.113241
## iter  30 value 77320.650477
## iter  40 value 77300.892460
## iter  50 value 77289.202169
## iter  60 value 77288.291045
## iter  70 value 77287.998993
## iter  80 value 77287.965345
## final  value 77287.950969 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 79457.676111 
## iter  10 value 77291.408958
## iter  20 value 77288.370641
## iter  30 value 77288.260680
## iter  40 value 77288.108589
## iter  50 value 77287.951384
## iter  50 value 77287.950817
## final  value 77287.949231 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 78568.664901 
## iter  10 value 77288.607762
## iter  20 value 77287.968189
## final  value 77287.949251 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79364.846930 
## iter  10 value 77319.332170
## iter  20 value 77286.384294
## final  value 77286.030663 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 79962.252312 
## iter  10 value 77304.467166
## iter  20 value 77286.212912
## final  value 77286.014579 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 79318.435712 
## iter  10 value 77308.063746
## iter  20 value 77286.254378
## final  value 77286.022820 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81966.450007 
## iter  10 value 77303.778243
## iter  20 value 77286.204969
## final  value 77286.013239 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 82007.545719 
## iter  10 value 77302.249518
## iter  20 value 77286.187344
## final  value 77286.042117 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 79730.532078 
## iter  10 value 77325.230222
## iter  20 value 77286.452294
## final  value 77286.025600 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80096.218037 
## iter  10 value 77309.570854
## iter  20 value 77286.271753
## final  value 77286.013417 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 80018.473881 
## iter  10 value 77329.109737
## iter  20 value 77286.497021
## final  value 77286.023589 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79788.712768 
## iter  10 value 77320.523980
## iter  20 value 77286.398034
## iter  30 value 77286.011411
## final  value 77286.009013 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 79349.345690 
## iter  10 value 77302.935083
## iter  20 value 77286.195248
## final  value 77286.011355 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80409.629433 
## iter  10 value 77319.128538
## iter  20 value 77286.381946
## final  value 77286.020437 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80243.823616 
## iter  10 value 77340.181110
## iter  20 value 77286.624666
## final  value 77286.039570 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 82793.323844 
## iter  10 value 77321.298346
## iter  20 value 77286.406962
## final  value 77286.065366 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 81051.507967 
## iter  10 value 77324.653515
## iter  20 value 77286.445645
## final  value 77286.012339 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 79039.348407 
## iter  10 value 77312.150694
## iter  20 value 77286.301497
## final  value 77286.007846 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79623.590455 
## final  value 77452.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 79772.606863 
## final  value 77452.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80958.330620 
## final  value 77452.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81551.108783 
## final  value 77452.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 82303.364554 
## final  value 77452.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81421.869912 
## final  value 77452.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80975.486541 
## final  value 77452.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 81344.283598 
## final  value 77452.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79940.659758 
## final  value 77452.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80112.597196 
## final  value 77452.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80043.520070 
## final  value 77452.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79039.799087 
## final  value 77452.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 80524.862712 
## final  value 77452.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 79532.205063 
## final  value 77452.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 81054.857599 
## final  value 77452.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80401.204911 
## iter  10 value 77460.505380
## final  value 77456.809755 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 81294.797562 
## iter  10 value 77457.628761
## final  value 77456.809778 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 81749.381145 
## iter  10 value 77462.225689
## iter  20 value 77460.156275
## final  value 77456.809313 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80533.891355 
## iter  10 value 77469.408925
## iter  20 value 77456.822720
## final  value 77456.808804 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80976.148102 
## iter  10 value 77459.895880
## final  value 77456.808779 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81159.730564 
## iter  10 value 77461.807982
## iter  20 value 77455.403876
## iter  30 value 77454.994429
## final  value 77454.723701 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 81635.951598 
## iter  10 value 77457.646197
## iter  20 value 77455.432651
## iter  30 value 77454.792700
## iter  40 value 77454.731889
## final  value 77454.723514 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 80274.522793 
## iter  10 value 77482.369752
## iter  20 value 77456.854677
## iter  30 value 77455.854707
## iter  40 value 77454.776493
## final  value 77454.751267 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79514.385877 
## iter  10 value 77455.714581
## iter  20 value 77454.767178
## iter  30 value 77454.730529
## final  value 77454.723617 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80189.475760 
## iter  10 value 77469.193992
## iter  20 value 77455.207972
## iter  30 value 77454.793179
## final  value 77454.723596 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 81871.295358 
## iter  10 value 77455.830848
## iter  20 value 77453.995448
## iter  30 value 77453.950859
## final  value 77453.947545 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80041.770743 
## iter  10 value 77455.009904
## iter  20 value 77454.360459
## iter  30 value 77453.979576
## final  value 77453.948579 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 80369.854811 
## iter  10 value 77456.594523
## iter  20 value 77454.848252
## iter  30 value 77454.571127
## iter  40 value 77454.272533
## final  value 77454.265720 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 81020.851066 
## iter  10 value 77488.686556
## iter  20 value 77455.200234
## iter  30 value 77454.336948
## iter  40 value 77454.276437
## iter  50 value 77454.004897
## final  value 77453.947798 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 78680.700739 
## iter  10 value 77470.184793
## iter  20 value 77454.465180
## iter  30 value 77454.228583
## iter  40 value 77454.032900
## final  value 77453.947836 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79827.885531 
## iter  10 value 77462.345695
## iter  20 value 77452.119278
## final  value 77452.022858 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 81999.037315 
## iter  10 value 77472.252583
## iter  20 value 77452.233496
## final  value 77452.048586 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80475.153044 
## iter  10 value 77474.653382
## iter  20 value 77452.261176
## final  value 77452.011445 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 82051.863911 
## iter  10 value 77469.765702
## iter  20 value 77452.204825
## final  value 77452.045355 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80000.610738 
## iter  10 value 77473.931371
## iter  20 value 77452.252851
## final  value 77452.012249 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81622.118768 
## iter  10 value 77467.466915
## iter  20 value 77452.178321
## final  value 77452.017358 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80915.525191 
## iter  10 value 77465.110551
## iter  20 value 77452.151154
## final  value 77452.024945 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 80152.758804 
## iter  10 value 77474.210502
## iter  20 value 77452.256070
## final  value 77452.043020 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 82516.083043 
## iter  10 value 77466.287536
## iter  20 value 77452.164724
## final  value 77452.010546 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 82318.418484 
## iter  10 value 77477.806171
## iter  20 value 77452.297525
## final  value 77452.020112 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80429.883824 
## iter  10 value 77484.430227
## iter  20 value 77452.373895
## final  value 77452.027324 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79906.161979 
## iter  10 value 77497.566490
## iter  20 value 77452.525346
## iter  30 value 77452.011178
## final  value 77452.009617 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 80759.798728 
## iter  10 value 77487.359922
## iter  20 value 77452.407672
## iter  30 value 77452.007032
## iter  30 value 77452.007029
## iter  30 value 77452.007026
## final  value 77452.007026 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 81759.137703 
## iter  10 value 77503.880601
## iter  20 value 77452.598143
## iter  30 value 77452.018127
## iter  30 value 77452.018092
## iter  30 value 77452.017834
## final  value 77452.017834 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 82037.624708 
## iter  10 value 77478.409895
## iter  20 value 77452.304485
## final  value 77452.022938 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 75538.473786 
## final  value 71353.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 75470.600579 
## final  value 71353.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 73565.187504 
## final  value 71353.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 74503.499236 
## final  value 71353.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 74342.305136 
## final  value 71353.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 74321.263138 
## final  value 71353.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 73143.693242 
## final  value 71353.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 73329.760160 
## final  value 71353.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 75593.909785 
## final  value 71353.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 74628.502002 
## final  value 71353.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 75684.525368 
## final  value 71353.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 72755.254482 
## final  value 71353.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 75366.760569 
## final  value 71353.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 75332.831227 
## final  value 71353.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 74605.392816 
## final  value 71353.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 74633.398389 
## iter  10 value 71358.877687
## final  value 71357.784093 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 73720.129966 
## iter  10 value 71361.711523
## iter  20 value 71357.787311
## final  value 71357.784044 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 73921.282583 
## iter  10 value 71358.732813
## iter  20 value 71357.790448
## final  value 71357.784055 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 72686.181643 
## iter  10 value 71358.816790
## iter  20 value 71357.792190
## final  value 71357.784108 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 75245.461379 
## iter  10 value 71360.879008
## iter  20 value 71357.785345
## final  value 71357.784130 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 74459.921312 
## iter  10 value 71363.068557
## iter  20 value 71357.697256
## iter  30 value 71355.724314
## final  value 71355.710625 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 73729.525512 
## iter  10 value 71356.417446
## iter  20 value 71355.756437
## final  value 71355.710314 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 74196.171799 
## iter  10 value 71376.749965
## iter  20 value 71356.642265
## iter  30 value 71355.976100
## iter  40 value 71355.715957
## final  value 71355.710314 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 73636.890794 
## iter  10 value 71362.121465
## iter  20 value 71355.873637
## iter  30 value 71355.711145
## iter  30 value 71355.711035
## iter  30 value 71355.711035
## final  value 71355.711035 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 74697.531462 
## iter  10 value 71358.039484
## iter  20 value 71355.919901
## iter  30 value 71355.712847
## final  value 71355.710893 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 73181.069844 
## iter  10 value 71360.353447
## iter  20 value 71355.022006
## iter  30 value 71354.939404
## iter  30 value 71354.939380
## iter  30 value 71354.939196
## final  value 71354.939196 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 75004.638457 
## iter  10 value 71439.384234
## iter  20 value 71355.509367
## iter  30 value 71354.964321
## final  value 71354.938542 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 72970.207946 
## iter  10 value 71361.058762
## iter  20 value 71355.127166
## final  value 71354.941886 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 75885.147015 
## iter  10 value 71394.970657
## iter  20 value 71356.927430
## iter  30 value 71355.913915
## iter  40 value 71355.014050
## final  value 71354.945874 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 73972.047338 
## iter  10 value 71599.476465
## iter  20 value 71356.349113
## iter  30 value 71355.910964
## iter  40 value 71355.608900
## iter  50 value 71355.433901
## iter  60 value 71355.287753
## final  value 71355.254538 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 73907.921190 
## iter  10 value 71366.205431
## iter  20 value 71353.152248
## final  value 71353.014820 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 75588.349216 
## iter  10 value 71372.318385
## iter  20 value 71353.222726
## final  value 71353.013229 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 74150.410425 
## iter  10 value 71367.572798
## iter  20 value 71353.168013
## final  value 71353.030797 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 75047.029880 
## iter  10 value 71364.582196
## iter  20 value 71353.133534
## final  value 71353.020310 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 73885.667836 
## iter  10 value 71364.180453
## iter  20 value 71353.128902
## final  value 71353.024318 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 75048.955210 
## iter  10 value 71379.877812
## iter  20 value 71353.309880
## final  value 71353.054306 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 75399.581496 
## iter  10 value 71363.366330
## iter  20 value 71353.119516
## final  value 71353.022648 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 73337.703378 
## iter  10 value 71393.314299
## iter  20 value 71353.464792
## final  value 71353.031506 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 73426.948003 
## iter  10 value 71372.308606
## iter  20 value 71353.222613
## final  value 71353.011791 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 74405.235682 
## iter  10 value 71398.784660
## iter  20 value 71353.527861
## final  value 71353.083212 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 73789.126465 
## iter  10 value 71404.091489
## iter  20 value 71353.589045
## final  value 71353.015031 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 74430.769593 
## iter  10 value 71400.237908
## iter  20 value 71353.544616
## final  value 71353.015176 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 73479.931812 
## iter  10 value 71387.543490
## iter  20 value 71353.398259
## final  value 71353.016166 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 76269.188784 
## iter  10 value 71379.839467
## iter  20 value 71353.309438
## final  value 71353.016509 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 76555.470502 
## iter  10 value 71392.105377
## iter  20 value 71353.450854
## final  value 71353.154914 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 73950.398952 
## final  value 70721.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 73523.794888 
## final  value 70721.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 73477.768064 
## final  value 70721.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 73428.266795 
## final  value 70721.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 73215.595366 
## final  value 70721.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 72486.793589 
## final  value 70721.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 72799.277502 
## final  value 70721.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 73767.755542 
## final  value 70721.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 73603.247255 
## final  value 70721.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 73573.965077 
## final  value 70721.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 72149.389999 
## final  value 70721.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 72353.810714 
## final  value 70721.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 73904.361973 
## final  value 70721.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 74236.091281 
## final  value 70721.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 75522.204830 
## final  value 70721.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 72854.006072 
## iter  10 value 70730.020074
## iter  20 value 70726.345602
## iter  30 value 70725.797589
## final  value 70725.788574 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 73438.484684 
## iter  10 value 70728.834954
## iter  20 value 70725.807394
## final  value 70725.788217 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 73829.135412 
## iter  10 value 70726.757414
## final  value 70725.788299 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 72853.011171 
## iter  10 value 70733.761416
## final  value 70725.788235 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 75335.267318 
## iter  10 value 70728.219521
## iter  20 value 70726.344260
## iter  30 value 70726.062959
## iter  40 value 70725.792757
## final  value 70725.788667 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 72854.178248 
## iter  10 value 70739.067621
## iter  20 value 70723.845909
## iter  30 value 70723.774912
## iter  40 value 70723.716256
## final  value 70723.712692 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 74345.325393 
## iter  10 value 70734.053388
## iter  20 value 70724.200763
## iter  30 value 70723.725378
## final  value 70723.712468 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 73870.102222 
## iter  10 value 70725.601740
## iter  20 value 70723.883428
## iter  30 value 70723.723247
## iter  30 value 70723.722812
## final  value 70723.712675 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 75150.334603 
## iter  10 value 70728.390142
## iter  20 value 70726.564559
## iter  30 value 70724.066214
## iter  40 value 70723.716807
## final  value 70723.712669 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 74642.901526 
## iter  10 value 70733.310206
## iter  20 value 70723.743761
## final  value 70723.712611 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 75723.998826 
## iter  10 value 70756.947624
## iter  20 value 70722.956843
## final  value 70722.940238 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 72940.764495 
## iter  10 value 70725.020392
## iter  20 value 70723.350603
## iter  30 value 70723.256650
## iter  40 value 70722.945640
## final  value 70722.940161 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 73929.887637 
## iter  10 value 70730.672113
## iter  20 value 70724.150713
## iter  30 value 70723.008424
## final  value 70722.940072 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 72372.141801 
## iter  10 value 70753.422918
## iter  20 value 70727.289816
## iter  30 value 70723.258266
## iter  40 value 70722.949515
## final  value 70722.941097 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 72865.704529 
## iter  10 value 70756.958199
## iter  20 value 70723.198838
## iter  30 value 70722.948067
## final  value 70722.939945 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 73086.949321 
## iter  10 value 70739.819314
## iter  20 value 70721.216972
## final  value 70721.027611 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 74118.005153 
## iter  10 value 70733.215266
## iter  20 value 70721.140832
## final  value 70721.023543 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 73511.500611 
## iter  10 value 70732.300419
## iter  20 value 70721.130285
## final  value 70721.023764 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 73178.596184 
## iter  10 value 70741.772665
## iter  20 value 70721.239493
## final  value 70721.012648 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 74383.929292 
## iter  10 value 70743.840293
## iter  20 value 70721.263331
## final  value 70721.013302 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 73796.313484 
## iter  10 value 70747.908491
## iter  20 value 70721.310234
## final  value 70721.050736 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 74859.305883 
## iter  10 value 70742.589795
## iter  20 value 70721.248913
## final  value 70721.047038 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 72667.735833 
## iter  10 value 70752.366739
## iter  20 value 70721.361634
## final  value 70721.011289 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 75486.536154 
## iter  10 value 70761.539595
## iter  20 value 70721.467390
## final  value 70721.032537 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 74696.514884 
## iter  10 value 70761.843439
## iter  20 value 70721.470893
## final  value 70721.023366 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 72157.491082 
## iter  10 value 70722.858752
## final  value 70721.140093 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 73066.627415 
## iter  10 value 70753.820156
## iter  20 value 70721.378391
## final  value 70721.015282 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 74626.190629 
## iter  10 value 70774.911671
## iter  20 value 70721.621559
## iter  30 value 70721.015185
## iter  30 value 70721.015178
## iter  30 value 70721.015172
## final  value 70721.015172 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 74654.292995 
## iter  10 value 70724.617296
## iter  20 value 70721.096855
## iter  20 value 70721.096519
## iter  20 value 70721.096321
## final  value 70721.096321 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 74718.237955 
## iter  10 value 70724.972819
## iter  20 value 70721.210723
## final  value 70721.042025 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79523.431227 
## final  value 76233.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80104.790331 
## final  value 76233.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 78884.742646 
## final  value 76233.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80527.715493 
## final  value 76233.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 79761.734359 
## final  value 76233.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 79322.914578 
## final  value 76233.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79142.035393 
## final  value 76233.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79473.552389 
## final  value 76233.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 80860.415100 
## final  value 76233.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 79863.897240 
## final  value 76233.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 78048.084473 
## final  value 76233.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 78696.994202 
## final  value 76233.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 79329.923008 
## final  value 76233.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 80849.736049 
## final  value 76233.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 80102.435538 
## final  value 76233.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79967.534485 
## iter  10 value 76244.669877
## iter  20 value 76237.974522
## final  value 76237.802570 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 78258.640060 
## iter  10 value 76237.851360
## final  value 76237.802645 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 78732.665518 
## iter  10 value 76239.923281
## final  value 76237.802625 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 79967.818119 
## iter  10 value 76237.958232
## final  value 76237.802788 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 78368.068188 
## iter  10 value 76239.394837
## iter  20 value 76237.816826
## final  value 76237.802861 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 79282.568432 
## iter  10 value 76256.641846
## iter  20 value 76236.411088
## final  value 76235.721446 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79652.961246 
## iter  10 value 76242.248047
## iter  20 value 76235.748461
## final  value 76235.720300 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79106.686986 
## iter  10 value 76243.656197
## iter  20 value 76235.754278
## final  value 76235.720464 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 77854.403632 
## iter  10 value 76256.865320
## iter  20 value 76235.835622
## final  value 76235.720280 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 79864.922134 
## iter  10 value 76243.719254
## iter  20 value 76236.450878
## iter  30 value 76235.768121
## final  value 76235.720406 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 78265.137895 
## iter  10 value 76270.112516
## iter  20 value 76235.231059
## iter  30 value 76234.955496
## final  value 76234.946097 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79530.580464 
## iter  10 value 76278.922099
## iter  20 value 76235.747761
## iter  30 value 76235.315057
## iter  40 value 76235.267606
## final  value 76234.950613 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 79301.415956 
## iter  10 value 76256.588216
## iter  20 value 76236.999785
## iter  30 value 76235.722820
## iter  40 value 76235.171844
## iter  50 value 76234.981980
## iter  60 value 76234.955036
## final  value 76234.945421 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 78006.933294 
## iter  10 value 76236.634397
## iter  20 value 76235.469191
## iter  30 value 76235.026682
## iter  40 value 76234.947533
## iter  40 value 76234.946873
## iter  40 value 76234.946455
## final  value 76234.946455 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 78921.858739 
## iter  10 value 76237.369529
## iter  20 value 76235.097908
## iter  30 value 76234.947346
## iter  30 value 76234.946778
## iter  30 value 76234.946096
## final  value 76234.946096 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 78804.695500 
## iter  10 value 76243.961377
## iter  20 value 76233.126376
## final  value 76233.024120 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80183.014647 
## iter  10 value 76244.312486
## iter  20 value 76233.130424
## final  value 76233.024142 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 78983.800728 
## iter  10 value 76244.461902
## iter  20 value 76233.132147
## final  value 76233.024152 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80031.842420 
## iter  10 value 76256.347213
## iter  20 value 76233.269175
## final  value 76233.011956 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 78682.581150 
## iter  10 value 76243.550844
## iter  20 value 76233.121643
## final  value 76233.022627 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 77868.437169 
## iter  10 value 76246.764073
## iter  20 value 76233.158689
## final  value 76233.010866 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 78855.369268 
## iter  10 value 76255.242460
## iter  20 value 76233.256438
## final  value 76233.013259 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 78188.323496 
## iter  10 value 76256.166418
## iter  20 value 76233.267091
## iter  30 value 76233.009195
## iter  30 value 76233.009048
## iter  30 value 76233.009045
## final  value 76233.009045 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79024.906778 
## iter  10 value 76261.893640
## iter  20 value 76233.333121
## final  value 76233.016635 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 78630.604575 
## iter  10 value 76256.678296
## iter  20 value 76233.272992
## final  value 76233.009932 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 78163.556469 
## iter  10 value 76267.803626
## iter  20 value 76233.401258
## iter  30 value 76233.009593
## iter  30 value 76233.009176
## iter  30 value 76233.009176
## final  value 76233.009176 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 81379.853151 
## iter  10 value 76278.574381
## iter  20 value 76233.525437
## final  value 76233.145720 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 78310.712477 
## iter  10 value 76277.868654
## iter  20 value 76233.517300
## final  value 76233.013200 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 79155.922640 
## iter  10 value 76236.313900
## iter  20 value 76233.153306
## final  value 76233.148897 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 78360.354869 
## iter  10 value 76235.222834
## iter  20 value 76233.116841
## final  value 76233.113533 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80591.977082 
## final  value 78278.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 81233.392562 
## final  value 78278.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80911.777822 
## final  value 78278.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 83096.784786 
## final  value 78278.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 81819.302348 
## final  value 78278.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81361.447415 
## final  value 78278.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 82270.178375 
## final  value 78278.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 81896.991187 
## final  value 78278.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 80433.373163 
## final  value 78278.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 82491.017545 
## final  value 78278.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80505.508040 
## final  value 78278.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80738.455976 
## final  value 78278.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 81080.887193 
## final  value 78278.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 79609.553777 
## final  value 78278.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 83019.164712 
## final  value 78278.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 82699.904525 
## iter  10 value 78287.264921
## iter  20 value 78282.814022
## iter  20 value 78282.813730
## iter  20 value 78282.813718
## final  value 78282.813718 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80703.043963 
## iter  10 value 78285.915418
## iter  20 value 78282.817403
## final  value 78282.813627 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 82338.144979 
## iter  10 value 78283.001092
## final  value 78282.813770 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81317.001654 
## iter  10 value 78286.643787
## iter  20 value 78282.822594
## final  value 78282.813612 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 79856.116849 
## iter  10 value 78284.416170
## final  value 78282.814060 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 82770.627348 
## iter  10 value 78282.310192
## iter  20 value 78280.843073
## final  value 78280.726593 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 81551.171423 
## iter  10 value 78280.953093
## final  value 78280.726317 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 81798.804868 
## iter  10 value 78296.785636
## iter  20 value 78281.402920
## iter  30 value 78280.762824
## final  value 78280.726454 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 81805.227134 
## iter  10 value 78284.133972
## iter  20 value 78280.781083
## final  value 78280.729542 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 83064.832318 
## iter  10 value 78285.174993
## iter  20 value 78280.912233
## final  value 78280.726289 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 81420.651170 
## iter  10 value 78296.402425
## iter  20 value 78281.343939
## iter  30 value 78280.829331
## iter  40 value 78280.312644
## iter  50 value 78279.957026
## final  value 78279.950928 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 81571.231604 
## iter  10 value 78283.231167
## iter  20 value 78280.058663
## final  value 78279.953292 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 82116.201464 
## iter  10 value 78309.222809
## iter  20 value 78280.826522
## iter  30 value 78280.463087
## final  value 78280.280398 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 80025.654702 
## iter  10 value 78298.676537
## iter  20 value 78280.643419
## iter  30 value 78280.286928
## iter  40 value 78279.985415
## final  value 78279.949672 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 81362.702613 
## iter  10 value 78281.366706
## iter  20 value 78279.955109
## final  value 78279.949470 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 82394.755724 
## iter  10 value 78300.436216
## iter  20 value 78278.258672
## final  value 78278.013720 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80817.300922 
## iter  10 value 78289.062698
## iter  20 value 78278.127544
## final  value 78278.023962 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 81591.693747 
## iter  10 value 78298.966046
## iter  20 value 78278.241722
## final  value 78278.016551 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 81455.531518 
## iter  10 value 78334.264888
## iter  20 value 78278.648690
## final  value 78278.042194 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80628.339374 
## iter  10 value 78288.304252
## iter  20 value 78278.118800
## final  value 78278.022972 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81894.214560 
## iter  10 value 78325.395323
## iter  20 value 78278.546431
## iter  30 value 78278.013314
## final  value 78278.010429 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 81578.580112 
## iter  10 value 78318.348505
## iter  20 value 78278.465187
## final  value 78278.020125 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 82000.546446 
## iter  10 value 78301.152577
## iter  20 value 78278.266931
## final  value 78278.041445 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 81215.352367 
## iter  10 value 78296.648467
## iter  20 value 78278.215002
## final  value 78278.012344 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 82174.274676 
## iter  10 value 78313.858608
## iter  20 value 78278.413422
## final  value 78278.020191 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 81198.796195 
## iter  10 value 78308.632511
## iter  20 value 78278.353169
## final  value 78278.042580 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 81353.425026 
## iter  10 value 78326.995586
## iter  20 value 78278.564881
## final  value 78278.022522 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 80761.010931 
## iter  10 value 78313.940132
## iter  20 value 78278.414362
## final  value 78278.011621 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 81245.467675 
## iter  10 value 78325.408122
## iter  20 value 78278.546578
## final  value 78278.009082 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 81110.866102 
## iter  10 value 78280.838521
## final  value 78278.084740 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80610.733489 
## final  value 76669.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 79813.501892 
## final  value 76669.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 78474.632774 
## final  value 76669.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80534.216746 
## final  value 76669.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 78894.192301 
## final  value 76669.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 78908.689756 
## final  value 76669.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 80211.220643 
## final  value 76669.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 78399.480503 
## final  value 76669.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 80510.666540 
## final  value 76669.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80571.694850 
## final  value 76669.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80867.421832 
## final  value 76669.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79751.100786 
## final  value 76669.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 80156.872894 
## final  value 76669.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 78463.086501 
## final  value 76669.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 79955.640106 
## final  value 76669.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79155.395961 
## iter  10 value 76677.428280
## final  value 76677.417902 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 79013.727725 
## iter  10 value 76765.509842
## iter  20 value 76675.306067
## iter  30 value 76673.820207
## final  value 76673.801364 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 79523.594683 
## iter  10 value 76673.825078
## final  value 76673.801436 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80303.234567 
## iter  10 value 76677.934108
## iter  20 value 76673.983029
## final  value 76673.801986 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 78696.312694 
## iter  10 value 76675.691967
## iter  20 value 76673.806908
## final  value 76673.801999 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 79947.033092 
## iter  10 value 76696.005193
## iter  20 value 76672.057326
## iter  30 value 76671.808026
## iter  40 value 76671.738105
## final  value 76671.719795 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 81089.208843 
## iter  10 value 76693.035732
## iter  20 value 76672.444961
## iter  20 value 76672.444425
## final  value 76671.719948 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 78409.742025 
## iter  10 value 76696.469982
## iter  20 value 76680.518111
## iter  30 value 76678.472308
## iter  40 value 76677.730136
## iter  50 value 76675.787571
## iter  60 value 76672.031112
## iter  70 value 76671.791208
## final  value 76671.719584 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79861.848148 
## iter  10 value 76674.844649
## iter  20 value 76671.947863
## final  value 76671.720875 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 79920.011895 
## iter  10 value 76675.023745
## iter  20 value 76671.773862
## final  value 76671.719867 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79422.012376 
## iter  10 value 76672.951259
## iter  20 value 76671.045114
## iter  30 value 76670.948688
## final  value 76670.944978 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 77728.015602 
## iter  10 value 76690.433556
## iter  20 value 76677.263085
## iter  30 value 76672.652459
## iter  40 value 76671.046931
## iter  50 value 76670.969451
## final  value 76670.945148 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 81442.326290 
## iter  10 value 76675.565079
## iter  20 value 76672.189690
## iter  30 value 76671.269612
## iter  40 value 76670.947112
## final  value 76670.945083 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 78603.535136 
## iter  10 value 76705.468763
## iter  20 value 76671.622154
## iter  30 value 76670.971479
## final  value 76670.949220 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 79950.136338 
## iter  10 value 76712.910924
## iter  20 value 76671.509703
## iter  30 value 76670.967072
## final  value 76670.944803 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80176.422858 
## iter  10 value 76686.156027
## iter  20 value 76669.197796
## final  value 76669.012322 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80661.016239 
## iter  10 value 76687.324287
## iter  20 value 76669.211265
## final  value 76669.026273 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 79179.672769 
## iter  10 value 76697.281343
## iter  20 value 76669.326062
## final  value 76669.020313 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 79549.833183 
## iter  10 value 76691.755677
## iter  20 value 76669.262355
## final  value 76669.030526 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 79768.901394 
## iter  10 value 76681.032655
## iter  20 value 76669.138727
## final  value 76669.022894 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 78967.909143 
## iter  10 value 76684.818583
## iter  20 value 76669.182376
## final  value 76669.032957 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79208.460447 
## iter  10 value 76712.343180
## iter  20 value 76669.499713
## iter  30 value 76669.009106
## final  value 76669.006336 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 80263.817856 
## iter  10 value 76704.553360
## iter  20 value 76669.409902
## final  value 76669.029863 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 81512.749895 
## iter  10 value 76688.759350
## iter  20 value 76669.227810
## final  value 76669.046282 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 78766.134972 
## iter  10 value 76703.949534
## iter  20 value 76669.402941
## iter  30 value 76669.009030
## iter  30 value 76669.008764
## final  value 76669.006036 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79214.300750 
## iter  10 value 76709.246950
## iter  20 value 76669.464016
## iter  30 value 76669.023641
## iter  30 value 76669.023007
## final  value 76669.019781 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 78141.739314 
## iter  10 value 76670.395682
## final  value 76669.069527 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 80670.401539 
## iter  10 value 76698.614293
## iter  20 value 76669.341430
## final  value 76669.017029 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 80344.076529 
## iter  10 value 76734.990985
## iter  20 value 76669.760824
## iter  30 value 76669.012631
## iter  30 value 76669.012430
## iter  30 value 76669.012425
## final  value 76669.012425 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 79954.915664 
## iter  10 value 76672.410600
## final  value 76669.124961 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 74751.903321 
## final  value 72104.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 73518.951771 
## final  value 72104.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 74915.179810 
## final  value 72104.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 75417.299364 
## final  value 72104.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 76205.837260 
## final  value 72104.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 74760.906070 
## final  value 72104.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 75629.936565 
## final  value 72104.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 74857.908749 
## final  value 72104.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 74900.693054 
## final  value 72104.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 75720.513208 
## final  value 72104.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 74492.857266 
## final  value 72104.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 74718.094587 
## final  value 72104.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 76767.979178 
## final  value 72104.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 74875.752106 
## final  value 72104.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 76789.696746 
## final  value 72104.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 75423.122781 
## iter  10 value 72108.972926
## final  value 72108.778834 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 75518.457862 
## iter  10 value 72120.790118
## final  value 72112.375171 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 75819.190428 
## iter  10 value 72110.280201
## final  value 72108.780862 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 74684.979315 
## iter  10 value 72108.999264
## final  value 72108.778664 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 75005.384946 
## iter  10 value 72110.318016
## final  value 72108.778429 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 73935.030733 
## iter  10 value 72145.785404
## iter  20 value 72118.806060
## iter  30 value 72106.778028
## final  value 72106.708831 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 73641.882745 
## iter  10 value 72108.191942
## iter  20 value 72107.521788
## iter  30 value 72106.890068
## iter  40 value 72106.710180
## final  value 72106.707957 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 73933.622854 
## iter  10 value 72109.048156
## iter  20 value 72106.790351
## final  value 72106.707487 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 75398.607804 
## iter  10 value 72127.443053
## iter  20 value 72107.364190
## final  value 72106.708102 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 75302.357352 
## iter  10 value 72128.899244
## iter  20 value 72106.723838
## final  value 72106.708505 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 75444.673760 
## iter  10 value 72117.346610
## iter  20 value 72106.804588
## iter  30 value 72106.046316
## final  value 72105.938048 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 75931.287347 
## iter  10 value 72108.276063
## iter  20 value 72106.651916
## iter  30 value 72106.331177
## iter  40 value 72106.286068
## iter  50 value 72106.091329
## iter  60 value 72105.938786
## final  value 72105.936512 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 75961.878363 
## iter  10 value 72108.202740
## iter  20 value 72106.479598
## iter  30 value 72106.248969
## iter  40 value 72105.982363
## iter  50 value 72105.937678
## iter  50 value 72105.937158
## iter  50 value 72105.936735
## final  value 72105.936735 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 76588.656826 
## iter  10 value 72107.119016
## iter  20 value 72106.268874
## iter  30 value 72106.077550
## iter  40 value 72105.950419
## final  value 72105.937060 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 75570.833344 
## iter  10 value 72108.675264
## iter  20 value 72106.311302
## iter  30 value 72106.059762
## iter  40 value 72105.940092
## final  value 72105.936819 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 75728.490305 
## iter  10 value 72126.491174
## iter  20 value 72104.259306
## final  value 72104.011803 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 75925.601633 
## iter  10 value 72125.604831
## iter  20 value 72104.249087
## final  value 72104.012657 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 73957.647907 
## iter  10 value 72119.445932
## iter  20 value 72104.178079
## final  value 72104.017452 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 76350.339833 
## iter  10 value 72121.496136
## iter  20 value 72104.201717
## final  value 72104.041651 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 75539.440096 
## iter  10 value 72126.972024
## iter  20 value 72104.264849
## final  value 72104.013340 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 77214.185670 
## iter  10 value 72122.193652
## iter  20 value 72104.209759
## iter  30 value 72104.022185
## iter  30 value 72104.022176
## iter  30 value 72104.022167
## final  value 72104.022167 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 75311.957874 
## iter  10 value 72145.740393
## iter  20 value 72104.481234
## iter  30 value 72104.011068
## iter  30 value 72104.010703
## final  value 72104.007727 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 76183.527919 
## iter  10 value 72180.572653
## iter  20 value 72104.882823
## final  value 72104.144119 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 74822.130819 
## iter  10 value 72149.593622
## iter  20 value 72104.525659
## final  value 72104.020962 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 75544.672428 
## iter  10 value 72139.685605
## iter  20 value 72104.411427
## final  value 72104.020443 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 75686.836062 
## iter  10 value 72160.569720
## iter  20 value 72104.652204
## iter  30 value 72104.013910
## iter  30 value 72104.013904
## iter  30 value 72104.013899
## final  value 72104.013899 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 75625.488492 
## iter  10 value 72156.756524
## iter  20 value 72104.608241
## final  value 72104.031491 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 76235.084693 
## iter  10 value 72159.937927
## iter  20 value 72104.644920
## final  value 72104.044411 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 74523.040642 
## iter  10 value 72146.759974
## iter  20 value 72104.492989
## iter  30 value 72104.009179
## final  value 72104.006443 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 76727.425053 
## iter  10 value 72128.441968
## iter  20 value 72104.281797
## iter  30 value 72104.019762
## iter  30 value 72104.019755
## iter  30 value 72104.019749
## final  value 72104.019749 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 83077.920273 
## final  value 79315.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 82852.990956 
## final  value 79315.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 82082.394861 
## final  value 79315.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 82731.750871 
## final  value 79315.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 81658.169756 
## final  value 79315.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81506.685013 
## final  value 79315.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 82562.141880 
## final  value 79315.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 84040.695469 
## final  value 79315.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 83423.266770 
## final  value 79315.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 83831.065502 
## final  value 79315.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 82180.748401 
## final  value 79315.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 82165.242666 
## final  value 79315.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 81520.560137 
## final  value 79315.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 83858.707368 
## final  value 79315.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 82283.724137 
## final  value 79315.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 83426.066160 
## iter  10 value 79323.615203
## iter  20 value 79319.845949
## final  value 79319.823677 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 83968.869424 
## iter  10 value 79324.067120
## iter  20 value 79319.835142
## final  value 79319.823607 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 83388.697215 
## iter  10 value 79322.464434
## final  value 79319.823703 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 83511.058060 
## iter  10 value 79321.581151
## iter  20 value 79319.829708
## final  value 79319.822938 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 81727.785740 
## iter  10 value 79323.526332
## iter  20 value 79319.903789
## final  value 79319.823162 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81791.538880 
## iter  10 value 79336.714256
## iter  20 value 79319.045345
## iter  30 value 79317.734711
## final  value 79317.732492 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 82925.709395 
## iter  10 value 79520.997926
## iter  20 value 79318.511939
## iter  30 value 79318.461290
## iter  30 value 79318.460993
## iter  30 value 79318.460956
## final  value 79318.460956 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 83076.149789 
## iter  10 value 79338.930036
## iter  20 value 79318.340909
## iter  30 value 79317.753352
## final  value 79317.731497 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 82236.501503 
## iter  10 value 79326.263796
## iter  20 value 79317.949279
## iter  30 value 79317.841284
## final  value 79317.731676 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 84481.074152 
## iter  10 value 79324.543198
## iter  20 value 79320.845382
## iter  30 value 79319.985887
## iter  40 value 79317.828703
## iter  50 value 79317.751846
## final  value 79317.731358 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 82364.668203 
## iter  10 value 79320.061517
## iter  20 value 79316.977541
## final  value 79316.954873 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80820.154684 
## iter  10 value 79349.576178
## iter  20 value 79319.051720
## iter  30 value 79317.503120
## iter  40 value 79317.045819
## final  value 79316.953242 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 82513.284684 
## iter  10 value 79319.559062
## iter  20 value 79316.970345
## final  value 79316.953735 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 82551.326021 
## iter  10 value 79336.262148
## iter  20 value 79318.732096
## iter  30 value 79317.266872
## iter  40 value 79316.963743
## final  value 79316.952882 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 81590.136679 
## iter  10 value 79317.477579
## iter  20 value 79317.051954
## iter  30 value 79316.961375
## final  value 79316.957227 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 82925.303594 
## iter  10 value 79342.245045
## iter  20 value 79315.314114
## final  value 79315.048850 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 82125.130621 
## iter  10 value 79349.698432
## iter  20 value 79315.400046
## final  value 79315.021732 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 83543.131803 
## iter  10 value 79326.211426
## iter  20 value 79315.129259
## final  value 79315.024229 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 82938.829667 
## iter  10 value 79331.075966
## iter  20 value 79315.185343
## final  value 79315.030634 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 82694.069782 
## iter  10 value 79327.848522
## iter  20 value 79315.148133
## final  value 79315.022530 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 81333.200413 
## iter  10 value 79347.026850
## iter  20 value 79315.369244
## final  value 79315.014412 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 84789.621403 
## iter  10 value 79337.637160
## iter  20 value 79315.260989
## final  value 79315.013940 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 83935.715315 
## iter  10 value 79353.143416
## iter  20 value 79315.439764
## iter  30 value 79315.010513
## final  value 79315.009542 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 83863.304763 
## iter  10 value 79334.855239
## iter  20 value 79315.228915
## final  value 79315.011851 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 83342.214521 
## iter  10 value 79340.009187
## iter  20 value 79315.288336
## final  value 79315.014068 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80076.116954 
## iter  10 value 79323.913641
## iter  20 value 79315.102767
## final  value 79315.006391 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 81372.887282 
## iter  10 value 79338.895850
## iter  20 value 79315.275500
## final  value 79315.017385 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 81281.047594 
## iter  10 value 79353.276676
## iter  20 value 79315.441300
## iter  30 value 79315.010550
## final  value 79315.009564 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 83832.889935 
## iter  10 value 79361.439859
## iter  20 value 79315.535415
## final  value 79315.028443 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 84288.327831 
## iter  10 value 79343.563562
## iter  20 value 79315.329315
## final  value 79315.049459 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 77438.696133 
## final  value 73740.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 76671.605068 
## final  value 73740.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 75920.876438 
## final  value 73740.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 78396.899064 
## final  value 73740.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 76182.730853 
## final  value 73740.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 76222.254205 
## final  value 73740.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 77905.605587 
## final  value 73740.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 75595.093453 
## final  value 73740.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 76702.404344 
## final  value 73740.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 76380.284649 
## final  value 73740.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 76677.351635 
## final  value 73740.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 75751.030865 
## final  value 73740.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 75348.105051 
## final  value 73740.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 76865.803150 
## final  value 73740.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 75487.420812 
## final  value 73740.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 77043.267380 
## iter  10 value 73748.628402
## iter  20 value 73744.881110
## final  value 73744.796675 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 77588.843469 
## iter  10 value 73744.805013
## final  value 73744.796706 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 76194.683141 
## iter  10 value 73745.167196
## final  value 73744.796627 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 76686.165027 
## iter  10 value 73748.447496
## iter  20 value 73744.910949
## final  value 73744.796454 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 75973.031341 
## iter  10 value 73747.762878
## iter  20 value 73744.851752
## final  value 73744.802489 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 77583.184519 
## iter  10 value 73746.220499
## iter  20 value 73742.845582
## iter  30 value 73742.726463
## final  value 73742.716947 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 77125.007617 
## iter  10 value 73745.548751
## iter  20 value 73742.750481
## final  value 73742.718506 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 76852.201297 
## iter  10 value 73760.924557
## iter  20 value 73742.921542
## iter  30 value 73742.718803
## final  value 73742.716881 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 77169.927872 
## iter  10 value 73747.633251
## iter  20 value 73743.027818
## iter  30 value 73742.718236
## final  value 73742.717072 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 76992.595031 
## iter  10 value 73751.979691
## iter  20 value 73744.501088
## iter  30 value 73742.874372
## final  value 73742.717069 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 76757.155223 
## iter  10 value 73753.277206
## iter  20 value 73742.446962
## iter  30 value 73742.059278
## iter  40 value 73742.001489
## iter  50 value 73741.947057
## final  value 73741.943133 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 76017.634573 
## iter  10 value 73746.044829
## iter  20 value 73742.921936
## iter  30 value 73742.090817
## iter  40 value 73741.954008
## final  value 73741.943770 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 76898.724758 
## iter  10 value 73747.845155
## iter  20 value 73742.230189
## iter  30 value 73741.953334
## final  value 73741.943053 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 76111.343776 
## iter  10 value 73748.036630
## iter  20 value 73742.137210
## iter  30 value 73741.956606
## final  value 73741.943010 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 75943.186908 
## iter  10 value 73746.486432
## iter  20 value 73742.201783
## iter  30 value 73741.943943
## iter  30 value 73741.943473
## iter  30 value 73741.943151
## final  value 73741.943151 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 77852.130321 
## iter  10 value 73760.020960
## iter  20 value 73740.230826
## final  value 73740.043022 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 77561.368426 
## iter  10 value 73760.982167
## iter  20 value 73740.241908
## final  value 73740.041169 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 77724.277248 
## iter  10 value 73762.008416
## iter  20 value 73740.253740
## final  value 73740.012338 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 76620.538657 
## iter  10 value 73751.627914
## iter  20 value 73740.134061
## final  value 73740.024129 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 77084.509004 
## iter  10 value 73764.010766
## iter  20 value 73740.276825
## final  value 73740.011976 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 74525.500042 
## iter  10 value 73747.135641
## iter  20 value 73740.082268
## final  value 73740.009179 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 78655.982075 
## iter  10 value 73764.622272
## iter  20 value 73740.283875
## final  value 73740.011603 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 77836.658303 
## iter  10 value 73752.784196
## iter  20 value 73740.147392
## final  value 73740.027258 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 75658.756844 
## iter  10 value 73756.410046
## iter  20 value 73740.189195
## iter  30 value 73740.013286
## iter  30 value 73740.013284
## iter  30 value 73740.012570
## final  value 73740.012570 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 76164.253044 
## iter  10 value 73779.354216
## iter  20 value 73740.453723
## final  value 73740.016673 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 76937.536564 
## iter  10 value 73744.477954
## iter  20 value 73740.206418
## iter  30 value 73740.037841
## iter  30 value 73740.037761
## iter  30 value 73740.037746
## final  value 73740.037746 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 77991.423586 
## iter  10 value 73767.893730
## iter  20 value 73740.321593
## final  value 73740.058220 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 77761.817519 
## iter  10 value 73782.828501
## iter  20 value 73740.493779
## final  value 73740.062198 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 75892.801765 
## iter  10 value 73742.554619
## final  value 73740.102992 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 77367.040902 
## iter  10 value 73786.803481
## iter  20 value 73740.539607
## iter  30 value 73740.023586
## iter  30 value 73740.023565
## iter  30 value 73740.023545
## final  value 73740.023545 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 65171.358609 
## final  value 62321.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 65961.027968 
## final  value 62321.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 65241.465720 
## final  value 62321.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 64726.383591 
## final  value 62321.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 64779.250876 
## final  value 62321.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 64952.642611 
## final  value 62321.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 65620.815543 
## final  value 62321.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 64086.793415 
## final  value 62321.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 67273.751026 
## final  value 62321.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 65725.771870 
## final  value 62321.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 65873.662549 
## final  value 62321.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 64602.033276 
## final  value 62321.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 64510.972530 
## final  value 62321.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 63717.524779 
## final  value 62321.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 64056.365109 
## final  value 62321.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 64991.209136 
## iter  10 value 62328.244498
## iter  20 value 62325.772030
## final  value 62325.766319 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 64668.466444 
## iter  10 value 62333.783826
## iter  20 value 62325.876812
## final  value 62325.766339 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 65697.433349 
## iter  10 value 62326.372338
## final  value 62325.766398 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 64859.060554 
## iter  10 value 62326.660638
## iter  20 value 62325.768552
## final  value 62325.766387 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 66453.566136 
## iter  10 value 62326.982689
## final  value 62325.766528 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 66479.081652 
## iter  10 value 62335.563270
## iter  20 value 62324.937720
## iter  30 value 62324.431830
## final  value 62324.423126 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 65339.876136 
## iter  10 value 62347.176905
## iter  20 value 62324.851289
## iter  30 value 62324.005122
## final  value 62323.701018 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 65066.007074 
## iter  10 value 62324.470726
## iter  20 value 62323.736729
## final  value 62323.700805 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 64389.527266 
## iter  10 value 62325.876930
## iter  20 value 62323.753878
## final  value 62323.703708 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 65999.594111 
## iter  10 value 62326.531945
## iter  20 value 62324.056479
## iter  30 value 62323.769638
## iter  40 value 62323.702997
## iter  40 value 62323.702517
## final  value 62323.701012 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 64367.509088 
## iter  10 value 62350.648282
## iter  20 value 62323.188717
## iter  30 value 62322.934273
## final  value 62322.932332 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 64806.023403 
## iter  10 value 62356.406329
## iter  20 value 62339.464596
## iter  30 value 62325.017056
## iter  40 value 62323.694067
## iter  50 value 62322.953688
## final  value 62322.934297 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 66493.544171 
## iter  10 value 62323.726664
## iter  20 value 62323.239973
## final  value 62322.931870 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 64798.212646 
## iter  10 value 62362.035292
## iter  20 value 62323.325408
## iter  30 value 62323.116238
## iter  40 value 62322.939807
## iter  40 value 62322.939545
## final  value 62322.931851 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 66331.955987 
## iter  10 value 62324.541475
## iter  20 value 62323.264513
## iter  30 value 62322.944309
## final  value 62322.932426 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 64975.075480 
## iter  10 value 62333.727493
## iter  20 value 62321.146738
## final  value 62321.028181 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 65741.370280 
## iter  10 value 62342.936471
## iter  20 value 62321.252910
## final  value 62321.043982 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 64537.444458 
## iter  10 value 62338.737045
## iter  20 value 62321.204494
## final  value 62321.013823 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 64668.429978 
## iter  10 value 62330.740808
## iter  20 value 62321.112304
## final  value 62321.023998 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 65859.391622 
## iter  10 value 62332.020313
## iter  20 value 62321.127056
## final  value 62321.024013 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 64798.010487 
## iter  10 value 62351.498819
## iter  20 value 62321.351627
## iter  30 value 62321.010911
## final  value 62321.008126 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 64712.249062 
## iter  10 value 62343.019563
## iter  20 value 62321.253868
## final  value 62321.017161 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 66128.708661 
## iter  10 value 62332.870628
## iter  20 value 62321.136859
## final  value 62321.017839 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 64023.129348 
## iter  10 value 62343.995845
## iter  20 value 62321.265124
## final  value 62321.009799 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 64637.993662 
## iter  10 value 62350.168268
## iter  20 value 62321.336287
## iter  30 value 62321.008826
## iter  30 value 62321.008823
## iter  30 value 62321.008819
## final  value 62321.008819 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 64538.850416 
## iter  10 value 62324.427037
## final  value 62321.137214 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 66061.099791 
## iter  10 value 62361.262265
## iter  20 value 62321.464192
## final  value 62321.013817 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 64048.960907 
## iter  10 value 62322.626129
## final  value 62321.065271 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 64458.464392 
## iter  10 value 62334.057539
## iter  20 value 62321.150543
## final  value 62321.019174 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 65435.548662 
## iter  10 value 62355.792685
## iter  20 value 62321.401132
## iter  30 value 62321.009590
## iter  30 value 62321.009437
## iter  30 value 62321.009433
## final  value 62321.009433 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 80253.693446 
## final  value 76292.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80904.579511 
## final  value 76292.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80718.068996 
## final  value 76292.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 79869.647558 
## final  value 76292.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 80496.287255 
## final  value 76292.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 78790.685023 
## final  value 76292.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79928.650681 
## final  value 76292.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79087.243555 
## final  value 76292.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79015.977448 
## final  value 76292.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 77981.183187 
## final  value 76292.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79158.397895 
## final  value 76292.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 78677.717865 
## final  value 76292.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 80108.974405 
## final  value 76292.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 78336.785773 
## final  value 76292.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 78858.309669 
## final  value 76292.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 78706.442720 
## iter  10 value 76296.797235
## iter  10 value 76296.796822
## iter  10 value 76296.796278
## final  value 76296.796278 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 79567.791644 
## iter  10 value 76298.396865
## iter  20 value 76296.837558
## final  value 76296.796327 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 79191.356597 
## iter  10 value 76299.273343
## iter  20 value 76297.067238
## iter  30 value 76296.798898
## iter  30 value 76296.798797
## final  value 76296.795916 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 80092.200139 
## iter  10 value 76299.675536
## final  value 76296.796318 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 79501.371460 
## iter  10 value 76300.971533
## iter  20 value 76297.623595
## final  value 76296.796544 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 78541.088178 
## iter  10 value 76319.237464
## iter  20 value 76295.468413
## iter  30 value 76294.957938
## iter  40 value 76294.717413
## iter  40 value 76294.717109
## iter  40 value 76294.716781
## final  value 76294.716781 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 78566.979406 
## iter  10 value 76313.416000
## iter  20 value 76294.908232
## final  value 76294.719181 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 78611.128284 
## iter  10 value 76300.625737
## iter  20 value 76297.101322
## iter  30 value 76295.063926
## final  value 76294.725383 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 78562.171626 
## iter  10 value 76297.563691
## iter  20 value 76294.985737
## final  value 76294.717403 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 80877.983773 
## iter  10 value 76298.041985
## iter  20 value 76294.829453
## iter  30 value 76294.720130
## final  value 76294.716728 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79612.071651 
## iter  10 value 76295.260839
## iter  20 value 76294.013867
## final  value 76293.943142 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 80620.105342 
## iter  10 value 76307.330888
## iter  20 value 76294.479936
## iter  30 value 76293.947756
## iter  30 value 76293.947029
## final  value 76293.943118 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 79954.381296 
## iter  10 value 76329.251502
## iter  20 value 76294.596617
## iter  30 value 76294.273736
## final  value 76294.261072 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 79101.752949 
## iter  10 value 76331.475013
## iter  20 value 76295.773364
## iter  30 value 76295.017578
## iter  40 value 76294.311087
## final  value 76294.260014 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 78851.891194 
## iter  10 value 76302.974225
## iter  20 value 76294.418499
## iter  30 value 76293.967766
## final  value 76293.944952 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79824.826853 
## iter  10 value 76303.881849
## iter  20 value 76292.136988
## final  value 76292.022607 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80300.902372 
## iter  10 value 76335.133029
## iter  20 value 76292.497290
## final  value 76292.081901 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 80724.118058 
## iter  10 value 76310.581396
## iter  20 value 76292.214229
## final  value 76292.044324 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 78676.435018 
## iter  10 value 76302.239573
## iter  20 value 76292.118054
## final  value 76292.022910 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 78706.219854 
## iter  10 value 76315.093175
## iter  20 value 76292.266246
## final  value 76292.050004 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 80908.421905 
## iter  10 value 76330.669900
## iter  20 value 76292.445834
## iter  30 value 76292.021744
## iter  30 value 76292.021442
## final  value 76292.020192 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 81171.860719 
## iter  10 value 76313.340925
## iter  20 value 76292.246044
## final  value 76292.049806 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 79011.669031 
## iter  10 value 76315.010400
## iter  20 value 76292.265292
## final  value 76292.011621 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 78562.767466 
## iter  10 value 76329.288228
## iter  20 value 76292.429904
## final  value 76292.010533 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 79207.316149 
## iter  10 value 76334.683843
## iter  20 value 76292.492111
## final  value 76292.030657 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 80970.482751 
## iter  10 value 76344.148629
## iter  20 value 76292.601233
## iter  30 value 76292.014397
## final  value 76292.011511 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79003.898708 
## iter  10 value 76319.424111
## iter  20 value 76292.316178
## final  value 76292.011380 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 78352.432939 
## iter  10 value 76310.468343
## iter  20 value 76292.212925
## final  value 76292.011266 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 79503.120327 
## iter  10 value 76340.971738
## iter  20 value 76292.564606
## iter  30 value 76292.027808
## final  value 76292.025251 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 79508.745702 
## iter  10 value 76328.440217
## iter  20 value 76292.420127
## final  value 76292.015439 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 77720.139916 
## final  value 75579.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 78584.262486 
## final  value 75579.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 77577.311762 
## final  value 75579.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 79422.273189 
## final  value 75579.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 79293.212519 
## final  value 75579.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 78815.577433 
## final  value 75579.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 78249.418631 
## final  value 75579.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 77581.995301 
## final  value 75579.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79194.294917 
## final  value 75579.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 78782.907909 
## final  value 75579.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79661.848845 
## final  value 75579.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 77703.659497 
## final  value 75579.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 77251.754784 
## final  value 75579.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 77469.462427 
## final  value 75579.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 77206.849927 
## final  value 75579.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 79270.730027 
## iter  10 value 75587.499172
## final  value 75583.799256 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 78479.642072 
## iter  10 value 75588.370216
## final  value 75583.799578 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 77737.589253 
## iter  10 value 75591.441439
## iter  20 value 75583.806976
## final  value 75583.799323 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 77678.582141 
## iter  10 value 75588.347133
## iter  20 value 75584.024996
## iter  30 value 75583.805672
## final  value 75583.799373 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 78909.228491 
## iter  10 value 75588.934374
## iter  20 value 75583.802745
## final  value 75583.799060 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 80044.794642 
## iter  10 value 75582.816504
## iter  20 value 75581.855708
## final  value 75581.718601 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79654.235356 
## iter  10 value 75678.082501
## iter  20 value 75582.568516
## iter  30 value 75581.753084
## iter  30 value 75581.752440
## final  value 75581.718347 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 78099.899087 
## iter  10 value 75599.538487
## iter  20 value 75583.181426
## iter  30 value 75581.966065
## final  value 75581.718807 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 79174.248151 
## iter  10 value 75587.658476
## iter  20 value 75581.923077
## final  value 75581.718392 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 76999.554750 
## iter  10 value 75584.468795
## iter  20 value 75581.718839
## iter  20 value 75581.718435
## iter  20 value 75581.718406
## final  value 75581.718406 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 77715.832411 
## iter  10 value 75637.670886
## iter  20 value 75581.614835
## iter  30 value 75580.954637
## final  value 75580.943998 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 79393.547021 
## iter  10 value 75584.380988
## iter  20 value 75581.503291
## iter  30 value 75581.042919
## iter  40 value 75580.946850
## iter  40 value 75580.946138
## final  value 75580.944453 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 79058.700811 
## iter  10 value 75583.955212
## iter  20 value 75581.435170
## iter  30 value 75580.984299
## iter  40 value 75580.946716
## iter  40 value 75580.946180
## final  value 75580.945144 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 76715.896349 
## iter  10 value 75616.184243
## iter  20 value 75581.699343
## iter  30 value 75581.284611
## iter  40 value 75581.083952
## iter  50 value 75580.951868
## final  value 75580.944738 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 77964.317208 
## iter  10 value 75617.963768
## iter  20 value 75581.493231
## iter  30 value 75580.947682
## final  value 75580.944470 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 78443.789817 
## iter  10 value 75601.292161
## iter  20 value 75579.257011
## final  value 75579.042707 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 80086.735819 
## iter  10 value 75597.239741
## iter  20 value 75579.210290
## final  value 75579.013609 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 79059.421263 
## iter  10 value 75591.115153
## iter  20 value 75579.139678
## final  value 75579.023051 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 79338.241197 
## iter  10 value 75603.485570
## iter  20 value 75579.282299
## final  value 75579.046358 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 78552.865004 
## iter  10 value 75599.932957
## iter  20 value 75579.241341
## final  value 75579.018740 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 77995.578328 
## iter  10 value 75604.796252
## iter  20 value 75579.297411
## final  value 75579.022508 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 79021.235250 
## iter  10 value 75614.639861
## iter  20 value 75579.410900
## final  value 75579.017776 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 80473.062698 
## iter  10 value 75604.317272
## iter  20 value 75579.291888
## final  value 75579.064498 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 78950.851737 
## iter  10 value 75596.856811
## iter  20 value 75579.205875
## final  value 75579.029279 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 79880.578584 
## iter  10 value 75595.456976
## iter  20 value 75579.189736
## final  value 75579.037634 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 79743.743389 
## iter  10 value 75625.482020
## iter  20 value 75579.535901
## iter  30 value 75579.012812
## iter  30 value 75579.012678
## iter  30 value 75579.012677
## final  value 75579.012677 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 78315.554018 
## iter  10 value 75621.211750
## iter  20 value 75579.486668
## final  value 75579.017882 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 77859.585189 
## iter  10 value 75620.738305
## iter  20 value 75579.481210
## iter  30 value 75579.010416
## iter  30 value 75579.010251
## final  value 75579.007858 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 76900.762287 
## iter  10 value 75594.130022
## iter  20 value 75579.174437
## final  value 75579.007989 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 77329.571831 
## iter  10 value 75580.718148
## iter  20 value 75579.053513
## iter  20 value 75579.053106
## iter  20 value 75579.053096
## final  value 75579.053096 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 74325.148932 
## final  value 72545.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 76028.544401 
## final  value 72545.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 74700.076835 
## final  value 72545.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 76249.439161 
## final  value 72545.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 75607.605815 
## final  value 72545.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 76001.857317 
## final  value 72545.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 75863.872199 
## final  value 72545.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 76940.044373 
## final  value 72545.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 75218.270733 
## final  value 72545.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 76459.402750 
## final  value 72545.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 74729.819185 
## final  value 72545.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 73610.264418 
## final  value 72545.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 75553.589361 
## final  value 72545.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 77389.730314 
## final  value 72545.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 76021.456089 
## final  value 72545.000000 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 76067.212574 
## iter  10 value 72556.413499
## iter  20 value 72550.583337
## iter  30 value 72549.808597
## iter  30 value 72549.808438
## final  value 72549.800209 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 76402.935598 
## iter  10 value 72551.221711
## final  value 72549.800228 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 75567.865032 
## iter  10 value 72551.518219
## iter  20 value 72549.814640
## iter  30 value 72549.801145
## iter  30 value 72549.800760
## iter  30 value 72549.800522
## final  value 72549.800522 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 74857.674339 
## iter  10 value 72551.031971
## iter  20 value 72549.813666
## final  value 72549.800859 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 75617.167440 
## iter  10 value 72553.718823
## iter  20 value 72549.824144
## final  value 72549.800931 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 74956.294093 
## iter  10 value 72572.559391
## iter  20 value 72547.725782
## final  value 72547.720933 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 76845.258273 
## iter  10 value 72550.877052
## iter  20 value 72547.748209
## final  value 72547.719202 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 76040.398881 
## iter  10 value 72549.895638
## iter  20 value 72547.753912
## final  value 72547.719196 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 76555.386232 
## iter  10 value 72571.407006
## iter  20 value 72549.025502
## iter  30 value 72547.768118
## iter  40 value 72547.722954
## final  value 72547.719206 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 74842.576507 
## iter  10 value 72558.773857
## iter  20 value 72547.863996
## iter  30 value 72547.720251
## iter  30 value 72547.719774
## iter  30 value 72547.719394
## final  value 72547.719394 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 76309.794782 
## iter  10 value 72573.747850
## iter  20 value 72547.955070
## iter  30 value 72547.023213
## final  value 72546.944415 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 77322.684416 
## iter  10 value 72581.910906
## iter  20 value 72548.073226
## iter  30 value 72547.524868
## iter  40 value 72547.184491
## iter  50 value 72546.949827
## final  value 72546.944792 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 75006.424594 
## iter  10 value 72592.126021
## iter  20 value 72547.192669
## iter  30 value 72546.945327
## iter  30 value 72546.944827
## iter  30 value 72546.944565
## final  value 72546.944565 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 74613.076061 
## iter  10 value 72637.575561
## iter  20 value 72612.120448
## iter  30 value 72577.643035
## iter  40 value 72555.894853
## iter  50 value 72549.148225
## iter  60 value 72546.314891
## iter  70 value 72545.914509
## iter  80 value 72545.317499
## iter  90 value 72544.979802
## iter 100 value 72544.795611
## final  value 72544.795611 
## stopped after 100 iterations
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 76230.223532 
## iter  10 value 72548.494923
## iter  20 value 72547.009083
## final  value 72546.945394 
## converged
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 75421.027702 
## iter  10 value 72568.617770
## iter  20 value 72545.272294
## final  value 72545.045458 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 75668.354134 
## iter  10 value 72557.540058
## iter  20 value 72545.144577
## final  value 72545.023680 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 75550.186323 
## iter  10 value 72567.226485
## iter  20 value 72545.256254
## final  value 72545.011327 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 74949.464839 
## iter  10 value 72564.329989
## iter  20 value 72545.222860
## final  value 72545.041713 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 76121.806337 
## iter  10 value 72556.949258
## iter  20 value 72545.137766
## final  value 72545.022736 
## converged
## Fitting Repeat 1 
## 
## # weights:  67
## initial  value 76720.387111 
## iter  10 value 72616.528070
## iter  20 value 72545.824663
## final  value 72545.059842 
## converged
## Fitting Repeat 2 
## 
## # weights:  67
## initial  value 76576.430513 
## iter  10 value 72577.981377
## iter  20 value 72545.380249
## iter  30 value 72545.011707
## final  value 72545.010186 
## converged
## Fitting Repeat 3 
## 
## # weights:  67
## initial  value 75341.587994 
## iter  10 value 72547.517443
## final  value 72545.068793 
## converged
## Fitting Repeat 4 
## 
## # weights:  67
## initial  value 75757.300556 
## iter  10 value 72581.253746
## iter  20 value 72545.417977
## final  value 72545.018012 
## converged
## Fitting Repeat 5 
## 
## # weights:  67
## initial  value 73901.480661 
## iter  10 value 72560.993776
## iter  20 value 72545.184396
## final  value 72545.009700 
## converged
## Fitting Repeat 1 
## 
## # weights:  111
## initial  value 74166.138723 
## iter  10 value 72574.675079
## iter  20 value 72545.342130
## final  value 72545.014685 
## converged
## Fitting Repeat 2 
## 
## # weights:  111
## initial  value 75176.436451 
## iter  10 value 72577.445970
## iter  20 value 72545.374077
## final  value 72545.009008 
## converged
## Fitting Repeat 3 
## 
## # weights:  111
## initial  value 73838.169732 
## iter  10 value 72561.232063
## iter  20 value 72545.187143
## final  value 72545.009807 
## converged
## Fitting Repeat 4 
## 
## # weights:  111
## initial  value 75211.529284 
## iter  10 value 72598.585331
## iter  20 value 72545.617797
## iter  30 value 72545.020043
## final  value 72545.016350 
## converged
## Fitting Repeat 5 
## 
## # weights:  111
## initial  value 76708.682657 
## iter  10 value 72590.493464
## iter  20 value 72545.524504
## final  value 72545.019454 
## converged
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Fitting Repeat 1 
## 
## # weights:  23
## initial  value 85502.978210 
## final  value 82564.000000 
## converged
## Fitting Repeat 2 
## 
## # weights:  23
## initial  value 84934.066001 
## final  value 82564.000000 
## converged
## Fitting Repeat 3 
## 
## # weights:  23
## initial  value 85099.312433 
## final  value 82564.000000 
## converged
## Fitting Repeat 4 
## 
## # weights:  23
## initial  value 86724.812746 
## final  value 82564.000000 
## converged
## Fitting Repeat 5 
## 
## # weights:  23
## initial  value 85650.713498 
## final  value 82564.000000 
## converged
## [1] "xgbTree"
## [1] "xgbLinear"
colnames(performetrics)[1]<- "Method"
colnames(performetrics)[2]<- "MAE"
colnames(performetrics)[3]<- "RMSE"

performetrics
##      Method      MAE     RMSE
## 1        rf 6.583256 10.10969
## 2       mlp 7.015946 11.08713
## 3     rpart 6.529211 10.28587
## 4 svmLinear 5.753177 10.32880
## 5 svmRadial 5.674722 10.39791
## 6     parRF 6.588219 10.03210
## 7    avNNet 6.936844 12.17846
## 8   xgbTree 6.605835 10.18660
## 9 xgbLinear 7.264238 11.54610
rm(i, control, methods, model_iq.cv, performetrics)

TIME SERIES ANALYSIS

ts_sj <- ts(sj_train_labels.lastna$total_cases, start = c(min(sj_train_labels.lastna$year),min(sj_train_labels.lastna$weekofyear[sj_train_labels.lastna$year == min(sj_train_labels.lastna$year)])), frequency = 52)

plot((ts_sj) , main = 'SJ: Total_cases')

plot(decompose(ts_sj))

Holt-Winters filtering

fit1 <- HoltWinters(ts_sj)
fit2<- HoltWinters(ts_sj, beta = FALSE, gamma = FALSE)
par(mfrow=c(2,1))
plot(fit1)
plot(fit2)